Lecture 5 and 6 Implicit Models -- GANs

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Oct 30, 2023

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±1S294-158 °eep 3Unsupervised !"Learning 00-ieter ±IIFbIIFbeel, ==:Xi (00-eter) ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan 663U² ´erkeley !"Lecture 5 & 6 ²mplicit #Models -- ³enerative ´dversarial $Networks (³´$Ns)
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 2
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline Ʋ #Motivation & °efinition of ²mplicit #Models ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 3
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s &&otivation: ·±''$ 00-rogress - ¶an ·oodfellow is first-author on the first ·±''$ paper - ·±''$ is most prominent of ¶mpliJJGcit &&odels 4
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s &&otivation: ´ig·±''$ @B@((+NiLg-0,''*735° ((+rToJHcPk° **-ToSnHFaMhuLJe° <8:NiRmToSnyHFaSn° ±²³#BDB 5
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s &&otivation: ·±''$ ±rt 6
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441So far... ■ ±utoregressive models ■ &&±µ¸, 00-ixel330R''$''$/²''$''$, ·ated 00-ixel²''$''$, 00-ixel441S''$±¶!%% ■ ¹low models ■ ±utoregressive ¹lows, ''$¶²¸, 330Real''$;;8V00-, ·low, ¹low++ ■ !%%atent ;;8VariaIIFble &&odels ■ ;;8V±¸, ¶<<9W±¸, ;;8V22/Q-;;8V±¸, ;;8V!%%±¸, 00-ixel;;8V±¸ ■ ²ommon aspeJJGct : !%%ikelihood-IIFbased models ■ exaJJGct (autoregressive and flows) ■ approximate (;;8V±¸) 7
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·enerative &&odels ■ 441Sample ■ ¸valuate likelihood ■ 552Train ■ 330Representation → <<9What if all we JJGcare aIIFbout is sampling? 8
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´uilding a sampler ■ ³ow aIIFbout this sampler? import glob± cv²± numpy as np files !!! glob³glob´‘µ³jpg’¶ def _sample´¶° idx !!! np³random³randint´len´files¶¶ return cv²³imread´files[idx]¶ def sample´µ± n_samples¶° samples !!! np³array´[_sample´¶ for _ in range´n_samples¶]¶ return samples 9
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´uilding a sampler ■ >>;You don’t just want to sample the exaJJGct data points you have. ■ >>;You want to IIFbuild a generative model that JJGcan understand the underlying distriIIFbution of data points and ■ smoothly interpolate aJJGcross the training samples ■ output samples similar IIFbut not the same as training data samples ■ output samples representative of the underlying faJJGctors of variation in the training distriIIFbution. ■ ¸xample: digits with unseen strokes, faJJGces with unseen poses, etJJGc. 10
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mpliJJGcit &&odels ■ 441Sample z from a fixed noise sourJJGce distriIIFbution (uniform or gaussian). ■ 00-ass the noise through a deep neural network to oIIFbtain a sample x. ■ 441Sounds familiar? 330Right: ■ ¹low &&odels ■ ;;8V±¸ Ŷ 7XRhDt’s WQgoYSic]nWQg to LQK OIYSiVPfVPfQKrQKc]nt XRhQKrQK? Ŷ =QKDrc]nYSic]nWQg tXRhQK OIQKQKp c]nQKurDa[l c]nQKtwor`Zk without QKxpa[lYSiMGYSit OIQKc]nsYSity QKstYSib\mDtYSioc]n 11
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mpliJJGcit &&odels ■ ■ ■ ■ ■ 12
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µeparture from maximum likelihood ■ ■ ■ ■ 13
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels Ʋ Original ³´$N (³oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 14
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·enerative ±dversarial ''$etworks 15
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·enerative ±dversarial ''$etworks ■ 552Two player minimax game IIFbetween generator (·) and disJJGcriminator (µ) ■ (µ) tries to maximize the log-likelihood for the IIFbinary JJGclassifiJJGcation proIIFblem - data: real (1) - generated: fake (0) ■ (·) tries to minimize the log-proIIFbaIIFbility of its samples IIFbeing JJGclassified as “fake” IIFby the disJJGcriminator (µ) 16
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·enerative ±dversarial ''$etworks 17 ¹igure from ''$eur¶00-441S 2016 ·±''$ 552Tutorial (·oodfellow)
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$s - 00-seudoJJGcode 18 [·oodfellow et al 2014]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$ 441See it in aJJGction: https://poloJJGcluIIFb.githuIIFb.io/ganlaIIFb/ 19
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$ samples from 2014 20 ¹igure from ·oodfellow et al 2014
663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ³ow to evaluate? ■ ¸valuation for ·±''$s is still an open proIIFblem ■ 663Unlike density models, you JJGcannot report expliJJGcit likelihood estimates on test sets. 21
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-arzen-<<9Window density estimator ■ ±lso known as $$ernel µensity ¸stimator ( $$µ¸) ■ ±n estimator with kernel $$and IIFbandwidth h: ■ ¶n generative model evaluation, $$is usually density funJJGction of standard ''$ormal distriIIFbution ´ishop 2006 22
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-arzen-<<9Window density estimator ■ ´andwidth h matters ■ ´andwidth h JJGchosen aJJGcJJGcording to validation set ´ishop 2006 23
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¸valuation 24 00-arzen <<9Window density estimates (·oodfellow et al, 2014)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-arzen-<<9Window density estimator ■ 00-arzen <<9Window estimator JJGcan IIFbe unreliaIIFble [± note on the evaluation of generative models (552Theis, ;;8Van den ))&ord, ´ethge 2015)] 25
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nJJGception 441SJJGcore ■ ²an we side-step high-dim density estimation? ■ ))&ne idea: good generators generate samples that are semantiJJGcally diverse ■ 441SemantiJJGcs prediJJGctor: trained ¶nJJGception ''$etwork v3 ■ p(y|x), y is one of the 1000 ¶mage''$et JJGclasses ■ ²onsiderations: ■ eaJJGch image x should have distinJJGctly reJJGcognizaIIFble oIIFbjeJJGct -> p(y|x) should have low entropy ■ there should IIFbe as many JJGclasses generated as possiIIFble -> p(y) should have high entropy 26
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nJJGception 441SJJGcore ■ ¶nJJGception model: ■ &&arginal laIIFbel distriIIFbution: ■ ¶nJJGception 441SJJGcore: [¶mproved ·±''$: 441Salimans et al 2016] 27
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nJJGception 441SJJGcore 28
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¹réJJGchet ¶nJJGception µistanJJGce ■ ¶nJJGception 441SJJGcore doesn’t suffiJJGciently measure diversity: a list of 1000 images (one of eaJJGch JJGclass) JJGcan oIIFbtain perfeJJGct ¶nJJGception 441SJJGcore ■ ¹¶µ was proposed to JJGcapture more nuanJJGces ■ ¸mIIFbed image x into some feature spaJJGce (2048-dimensional aJJGctivations of the ¶nJJGception-v3 pool3 layer), then JJGcompare mean (m) & JJGcovarianJJGce (²) of those random features [³eusel et al, 2017] 29
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¹réJJGchet ¶nJJGception µistanJJGce [³eusel et al, 2017] 30
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¹réJJGchet ¶nJJGception µistanJJGce [³eusel et al, 2017] 31
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·enerative ±dversarial ''$etworks ■ ey pieJJGces of ·±''$ ■ ¹ast sampling ■ ''$o inferenJJGce ■ ''$otion of optimizing direJJGctly for what you JJGcare aIIFbout - perJJGceptual samples 32
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet Ŷ 1Some 2Theory: ±ayes-optimal ²iscriminator; °ensen-1Shannon ²ivergence; #Mode ³ollapse; ´voiding 1Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 33
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$: ´ayes-))&ptimal µisJJGcriminator ■ <<9What’s the optimal disJJGcriminator given generated and true distriIIFbutions? 34
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$: ´ayes-))&ptimal µisJJGcriminator 35 µisJJGcriminator µata distriIIFbution &&odel / ·enerator distriIIFbution [¹igure 441SourJJGce: ·oodfellow ''$eur¶00-441S 2016 552Tutorial on ·±''$s]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$: ·enerator ))&IIFbjeJJGctive under ´ayes-))&ptimal µisJJGcriminator µ* ? 36
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ehaviors aJJGcross divergenJJGce measures 37 [“± note on the evaluation of generative models” -- 552Theis, ;;8Van den ))&ord, ´ethge 2015]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µireJJGction of $$!%% divergenJJGce 38 µeep !%%earning 552TextIIFbook (·oodfellow 2016)- ²hapter 3
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s !%% and °441Sµ 39
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s &&ode JJGcovering vs &&ode seeking: 552Tradeoffs ■ ¹or JJGcompression, one would prefer to ensure all points in the data distriIIFbution are assigned proIIFbaIIFbility mass. ■ ¹or generating good samples, IIFblurring aJJGcross modes spoils perJJGceptual quality IIFbeJJGcause regions outside the data manifold are assigned non-zero proIIFbaIIFbility mass. ■ 00-iJJGcking one mode without assigning proIIFbaIIFbility mass on points outside JJGcan produJJGce “IIFbetter-looking” samples. ■ ²aveat : &&ore expressive density models JJGcan plaJJGce proIIFbaIIFbility mass more aJJGcJJGcurately. ¸xample: 663Using mixture of ·aussians as opposed to a single isotropiJJGc gaussian. 40
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s &&ode ²ollapse 41 441Standard ·±''$ training JJGcollapses when the true distriIIFbution is a mixture of gaussians (¹igure from &&etz et al 2016)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´aJJGck to ·±''$s #Mini-µxercise ■ ¶s it feasiIIFble to run the inner optimization to JJGcompletion? ■ ¹or this speJJGcifiJJGc oIIFbjeJJGctive, would it JJGcreate proIIFblems if we were aIIFble to do so? 42 0Recall
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µisJJGcriminator 441Saturation ■ ·enerator samples JJGconfidently JJGclassified as fake IIFby the disJJGcriminator reJJGceive no gradient for the generator update. 43
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ±voiding µisJJGcriminator 441Saturation: (1) ±lternating ))&ptimization ■ ±lternate gradient steps on disJJGcriminator and generator oIIFbjeJJGctives ■ ´alanJJGcing these two updates is hard for the zero-sum game 44
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ±voiding µisJJGcriminator 441Saturation: (2) ''$on 441Saturating ¹ormulation 45 ''$ot zero-sum
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ±voiding µisJJGcriminator 441Saturation: (2) ''$on 441Saturating ¹ormulation 46 ■ ))&330R¶·¶''$±!%% ¶441S441S663U¸: ·enerator samples JJGconfidently JJGclassified as fake IIFby the disJJGcriminator reJJGceive no gradient for the generator update. ■ ¹¶==:X: non-saturating loss for when disJJGcriminator JJGconfident aIIFbout fake
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation Ʋ ³´$N Progression: Ŷ ²³ µ´$N (0Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 47
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µeep ²onvolutional ·±''$ (µ²·±''$) 48
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µeep ²onvolutional ·±''$ (µ²·±''$) 49 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - ±rJJGchiteJJGcture µesign ■ 441Supervised !%%earning ²''$''$s not direJJGctly usaIIFble ■ 330Remove max-pooling and mean-pooling ■ 663Upsample using transposed JJGconvolutions in the generator ■ µownsample with strided JJGconvolutions and average pooling ■ ''$on-!%%inearity: 330Re!%%663U for generator, !%%eaky-330Re!%%663U (0.2) for disJJGcriminator ■ ))&utput ''$on-!%%inearity: tanh for ·enerator, sigmoid for disJJGcriminator ■ ´atJJGch ''$ormalization used to prevent mode JJGcollapse ■ ´atJJGch ''$ormalization is not applied at the output of · and input of µ ■ ))&ptimization details ■ ±dam: small !%%330R - 2e-4; small momentum: 0.5, IIFbatJJGch-size: 128 50 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ ´atJJGch ''$orm 51 ²hintala 2016
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - $$ey 330Results ■ ·ood samples on datasets with 3&& images (¹aJJGces, ´edrooms) for the first time 52 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - $$ey 330Results 53 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - $$ey 330Results ■ 441Smooth interpolations in high dimensions 54 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - $$ey 330Results ■ ¶magenet samples 55 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - $$ey 330Results ■ ;;8VeJJGctor ±rithmetiJJGc 56 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - $$ey 330Results 57 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - $$ey 330Results 58 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - $$ey 330Results 330Representation !%%earning 59 [330Radford et al 2016]
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s µ²·±''$ - ²onJJGclusions ■ ¶nJJGcrediIIFble samples for any generative model ■ ·±''$s JJGcould IIFbe made to work well with arJJGchiteJJGcture details ■ 00-erJJGceptually good samples and interpolations ■ 330Representation !%%earning Ʋ ProFblems to address: ■ 663UnstaIIFble training ■ ´rittle arJJGchiteJJGcture / hyperparameters 60
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation Ʋ ³´$N Progression: ■ µ² ·±''$ (330Radford et al, 2016) Ŷ ¶mproved 2Training of µ´$Ns (1Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 61
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ ¹eature &&atJJGching ■ &&iniIIFbatJJGch disJJGcrimination ■ ³istoriJJGcal ±veraging ■ ;;8Virtual IIFbatJJGch normalization ■ ))&ne-sided laIIFbel smoothing 62 441Salimans 2016
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ ¹eature &&atJJGching 63 ·enerator oIIFbjeJJGctive 441Salimans 2016
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ &&iniIIFbatJJGch disJJGcrimination 64 ±llows to inJJGcorporate side information from other samples and is superior to feature matJJGching in the unJJGconditional setting. ³elps addressing mode JJGcollapse IIFby allowing disJJGcriminator to deteJJGct if the generated samples are too JJGclose to eaJJGch other. 441Salimans 2016
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ ³istoriJJGcal ±veraging 65 441Salimans 2016
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ ))&ne-sided laIIFbel smoothing 66 ¹igure sourJJGce: ''$eur¶00-441S tutorial ·oodfellow 2016
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ <<9Why one-sided? 67 ¹igure sourJJGce: ''$eur¶00-441S tutorial ·oodfellow 2016
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ ;;8Virtual ´atJJGch ''$ormalization 68 ¹igure sourJJGce: ''$eur¶00-441S tutorial ·oodfellow 2016
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ ;;8Virtual ´atJJGch ''$ormalization ■ 663Use a referenJJGce IIFbatJJGch (fixed) to JJGcompute normalization statistiJJGcs ■ ²onstruJJGct a IIFbatJJGch JJGcontaining the sample and referenJJGce IIFbatJJGch 69
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ 441Semi-441Supervised !%%earning ■ 00-rediJJGct laIIFbels in addition to fake/real in the disJJGcriminator ■ ±pproximate way of modeling p(x,y) ■ ·enerator doesn’t have to IIFbe made JJGconditional p(x|y) ■ 663Use a deeper arJJGchiteJJGcture for the disJJGcriminator JJGcompared to generator 70
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s ■ ¶nJJGception 441SJJGcore ■ ■ ±pproximate way of modeling p(x,y) ■ ²orrelates with human judgement ■ ²aptures some neJJGcessity for diversity 71
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mproved training of ·±''$s 72 441Salimans 2016
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation Ʋ ³´$N Progression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) Ŷ 9Wµ´$N, 9Wµ´$N-µP, Progressive µ´$N, 1S$N-µ´$N, 1S´µ´$N ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 73
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9Wasserstein µistanJJGce ■ ■ ±nother distanJJGce measure inspired from ))&ptimal 552Transport is the ¸arth &&over (¸&&) distanJJGce ■ ■ ·oal: µesign a ·±''$ oIIFbjeJJGctive funJJGction suJJGch that the generator minimizes the ¸arth &&over / <<9Wasserstein distanJJGce IIFbetween data and generated distriIIFbutions. 74
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s antoroviJJGch 330RuIIFbinstein µuality ■ ■ ¶ntraJJGctaIIFble to estimate ■ ■ 441SearJJGch over joint distriIIFbutions is now a searJJGch over 1-!%%ipsJJGchitz funJJGctions 75
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9Wasserstein ·±''$ ±nother distanJJGce measure inspired from ))&ptimal 552Transport is the ¸arth &&over (¸&&) distanJJGce ■ ■ 441Supremum over linear (funJJGction spaJJGce) expeJJGctations => searJJGch over $$-!%%ipsJJGchitz gives you $$times the <<9Wasserstein distanJJGce. 76
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9Wasserstein ·±''$ ■ ■ ■ ■ 77
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9Wasserstein ·±''$ - 00-seudoJJGcode 78 (±rjovsky et al 2017)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9Wasserstein ·±''$ - 552Training JJGcritiJJGc to JJGconverge 79 (±rjovsky et al 2017)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9Wasserstein distanJJGce JJGcorrelates with sample quality 80 (±rjovsky et al 2017) <<9Wasserstein ¸stimate °441Sµ ¸stimate
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$ 441Samples on par with µ²·±''$ 81 (±rjovsky et al 2017)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$ roIIFbust to arJJGchiteJJGcture JJGchoiJJGces 82 (±rjovsky et al 2017)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$ roIIFbust to arJJGchiteJJGcture JJGchoiJJGces 83 (±rjovsky et al 2017)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$ 441Summary 84 (±rjovsky et al 2017) 441Standard ·±''$ <<9Wasserstein ·±''$
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$ 441Summary ■ ''$ew divergenJJGce measure for optimizing the generator ■ ±ddresses instaIIFbilities with °441Sµ version (sigmoid JJGcross entropy) ■ 330RoIIFbust to arJJGchiteJJGctural JJGchoiJJGces ■ 00-rogress on mode JJGcollapse and staIIFbility of derivative wrt input ■ ¶ntroduJJGces the idea of using lipsJJGchitzness to staIIFbilize ·±''$ training ■ ''$egative : 85 (±rjovsky et al 2017)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation Ʋ ³´$N Progression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, 9Wµ´$N-µP , 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 86
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: ·radient 00-enalty for !%%ipsJJGchitzness 87 ·ulrajani et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: ·radient 00-enalty for !%%ipsJJGchitzness 88 ·ulrajani et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: ·radient 00-enalty for !%%ipsJJGchitzness 89 ·ulrajani et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: 00-seudoJJGcode 90 ·ulrajani et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: ´atJJGch''$orm 91 ·ulrajani et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: 330RoIIFbustness to arJJGchiteJJGctures 92 ·ulrajani et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: 330RoIIFbustness to arJJGchiteJJGctures 93 ·ulrajani et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: ³igh quality samples 94 ·ulrajani et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: ³igh quality samples 95 ·ulrajani et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s <<9W·±''$-·00-: 441Summary ■ 330RoIIFbustness to arJJGchiteJJGctural JJGchoiJJGces ■ ´eJJGcame a very popular ·±''$ model - 2000+ JJGcitations, has IIFbeen used in ''$;;8V¶µ¶±’s 00-rogressive ·±''$s, 441Style·±''$, etJJGc - IIFbiggest ·±''$ suJJGcJJGcesses ■ 330Residual arJJGchiteJJGcture widely adopted. ■ 00-ossiIIFble negative- slow wall JJGcloJJGck time due to gradient penalty. ■ ·radient penalty applied on a heuristiJJGc distriIIFbution of samples from JJGcurrent generator. ²ould IIFbe unstaIIFble when learning rates are high. 96
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation Ʋ ³´$N Progression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, Progressive µ´$N , 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 97
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-rogressive growing of ·±''$s 98 arras et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-rogressive growing of ·±''$s 99 arras et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-rogressive growing of ·±''$s 100 arras et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-rogressive growing of ·±''$s 101 arras et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-rogressive growing of ·±''$s 102 a r r a s e t a l 2 0 1 7
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-rogressive growing of ·±''$s 103 arras et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-rogressive growing of ·±''$s 104
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation Ʋ ³´$N Progression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 1S$N-µ´$N , 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 105
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441SpeJJGctral ''$ormalization ·±''$ (441S''$·±''$) 106 &&iyato et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441SpeJJGctral ''$ormalization ·±''$ (441S''$·±''$) 107 &&iyato et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441SpeJJGctral ''$ormalization ·±''$ (441S''$·±''$) ■ ey idea: ²onneJJGcting !%%ipsJJGchitzness of disJJGcriminator to speJJGctral norm of eaJJGch layer. 108 &&iyato et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441SpeJJGctral ''$ormalization ·±''$ (441S''$·±''$) 109 &&iyato et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441SpeJJGctral ''$ormalization ·±''$ (441S''$·±''$) 110 &&iyato et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441SpeJJGctral ''$ormalization ·±''$ (441S''$·±''$) 111 &&iyato et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441SpeJJGctral ''$ormalization ·±''$ (441S''$·±''$) 112 &&iyato et al 2017
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441SpeJJGctral ''$ormalization ·±''$ (441S''$·±''$) 113 · &&atch Norm for convolution layers def batch_normalization´x± µ± eps!!!¸e¹º¶° · x in N,,HW'' with tf³variable_scope´‘ conv_bn ’¶° mean± var !!! tf³nn³moments´x± [ » ± ¸ ± ² ]± keep_dims !!!88True u !!! ´x ¹ mean¶µtf³rsqrt´var ¼ epsilon¶ scale± offset !!! tf³split´tf³get_variable´ ‘scale_offset’ ± [ ¸ ± ¸ ± ¸ ± x³shape[ ¹¸ ]³value] µ ² ¶¶ return u µ scale ¼ offset
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ´atJJGch ''$ormalization 114 · ''onditional &&atch Norm for convolution layers def conditional_batch_normalization´x± y± µ± eps!!!¸e¹º¶° · x in N,,HW'' · x° ½(( [b± h± w± c]¾ y° ²(( [b± num_classes] ´one¹hot encoded¶ with tf³variable_scope´‘ cond_conv_bn ’¶° mean± var !!! tf³nn³moments´x± [ » ± ¸ ± ² ]± keep_dims !!!88True u !!! ´x ¹ mean¶µtf³rsqrt´var ¼ epsilon¶ scale± offset !!! tf³split´ tf³layers³dense´y± ² µ x³shape[ ¹¸ ]³value¶± ² ± ¹¸ return u µ scale ¼ offset
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ´atJJGch ''$ormalization with speJJGc-norm 115 · ''onditional &&atch Norm ´with 77SN¶ for convolution layers def conditional_batch_normalization´x± y± µ± eps!!!¸e¹º¶° · x in N,,HW'' · x° ½(( [b± h± w± c]¾ y° ²(( [b± num_classes] ´one¹hot encoded¶ with tf³variable_scope´‘ cond_conv_bn ’¶° mean± var !!! tf³nn³moments´x± [ » ± ¸ ± ² ]± keep_dims !!!88True u !!! ´x ¹ mean¶µtf³rsqrt´var ¼ epsilon¶ scale± offset !!! tf³split´ dense´y± ² µ x³shape[ ¹¸ ]³value± use_sn !!! 88True ¶± ² ± ¹¸ return u µ scale ¼ offset
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-ower ¶teration to implement speJJGc-norm 116 · Power --Iteration def spec_norm ´w_± µ± n_iterations !!! ¸ ¶° w !!! tf³reshape´w_± [ ¹¸ ± w³shape[ ¹¸ ]¶ u !!! tf³get_variable´ ‘u’ ± shape!!!´w³shape[ » ]± ¸ ¶± trainable!!! **alse for _ in range´n_iterations¶° v !!! tf³nn³l²_normalize´tf³matmul´w± u± transpose_a !!! 88True ¶± epsilon¶ u !!! tf³nn³l²_normalize´tf³matmul´w± v¶± epsilon¶ u± v !!! tf³stop_gradient´u¶± tf³stop_gradient´v¶ norm_value !!! tf³matmul´tf³matmul´tf³transpose´u¶± w¶± v¶ w_normalized !!! tf³reshape´w ¿ norm_value± w_³shape¶ return w_normalized
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-ower ¶teration to implement speJJGc-norm 117 · Power --Iteration def dense ´x± µ± n_out ± name !!! ’dense’ ± sn_iters !!! ¸ ± use_sn !!! 88True ¶° with tf³get_variable´ name ¶° w !!! tf³get_variable´ ‘w’ ± [x³shape[ ¹¸ ]³value± n_out]¶ if use_sn ° w !!! spec_norm´w± n_iterations!!! sn_iters b !!! tf³get_variable´ ‘b’ ± [ n_out ]± initializer !!! tf³zeros_initializer´¶ return tf³nn³bias_add´tf³matmul´x± w¶± b¶
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-ower ¶teration to implement speJJGc-norm 118 · Power --Iteration def conv²d ´x± µ± n_out± kernels± strides± name± sn_iters!!!¸± use_sn!!! 88True ¶° with tf³get_variable´ name ¶° w !!! tf³get_variable´ ‘w’ ± [ µkernels ± x³shape[¹¸]³value± n_out ]¶ if use_sn ° w !!! spec_norm´w± n_iterations!!! sn_iters b !!! tf³get_variable´ ‘b’ ± [ n_out ]± initializer !!! tf³zeros_initializer´¶ return tf³nn³bias_add´tf³nn³conv²d´x± w± [¸± µstrides ± ¸]¶
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441S''$·±''$: 441Summary ■ ³igh quality JJGclass JJGconditional samples at ¶magenet sJJGcale ■ ¹irst ·±''$ to work on full ¶magenet (million image dataset) ■ ²omputational IIFbenefits over <<9W·±''$-·00- (single power iteration and no need of a IIFbaJJGckward pass) 119
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441S''$·±''$: ²omputational ´enefits 120
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 00-rojeJJGction µisJJGcriminator 121
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation Ʋ ³´$N Progression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 1S´µ´$N ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 122
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Self ±ttention ·±''$ (441S±·±''$) 123
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Self ±ttention ·±''$ (441S±·±''$) Zhang et al 2018 124
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Self ±ttention ·±''$ (441S±·±''$) Zhang et al 2018 125
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Self ±ttention ·±''$ (441S±·±''$) ■ 441Salient IIFbits: ■ ±pplies speJJGctral normalization to IIFboth the generator and disJJGcriminator weight matriJJGces ■ 552This is JJGcounter-intuitive to popular IIFbelief that you only have to mathematiJJGcally JJGcondition the disJJGcriminator ■ 663Uses self-attention in IIFboth the generator and disJJGcriminator ■ ³inge !%%oss ■ ¹irst ·±''$ to produJJGce “good” unJJGconditional full ¶magenet samples ■ ²onditional models ■ ²onditional ´''$ for ·, 00-rojeJJGction µisJJGcriminator for µ 126
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Self ±ttention ·±''$ (441S±·±''$) Zhang et al 2018 127
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Self ±ttention ·±''$ (441S±·±''$) Zhang et al 2018 128
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Self ±ttention ·±''$ (441S±·±''$) Zhang et al 2018 129
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation Ʋ ³´$N Progression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ Ŷ ±igµ´$N, ±igµ´$N-²eep, 1Styleµ´$N, 1Styleµ´$N-v2, 8V¶±-µ´$N ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 130
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ 131
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ 132
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ 133
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ and ´ig·±''$-deep 134
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ 135
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$-deep 136
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ 137
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ ■ 441Salient IIFbits ■ ¶nJJGcrease your IIFbatJJGch size (as muJJGch as you JJGcan) ■ 663Use ²ross-330RepliJJGca (441SynJJGc) ´atJJGch ''$orm ■ ¶nJJGcrease your model size ■ <<9Wider helps as muJJGch as deeper ■ ¹use JJGclass information at all levels ■ ³inge !%%oss ■ ))&rthonormal regularization & 552TrunJJGcation 552TriJJGck 138
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ 139
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ 140
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ - 552TrunJJGcation 552TriJJGck 141
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ - 441Sampling 142
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ 143
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig·±''$ 144
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Style·±''$ 145
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Style·±''$ - ±daptive ¶nstanJJGce ''$orm 146
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Style·±''$ - 441Style 552Transfer 147
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Style·±''$ 148
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Style·±''$ - ¸ffeJJGct of adding noise 149
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Style·±''$-v2 150
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Style·±''$-v2 151
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Style·±''$-v2 152
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nformation ´ottleneJJGck ;;8Variational ¶nformation ´ottleneJJGck [ ±lemi et al., 2016 ] ;;8Variational ¶nformation ´ottleneJJGck ·±''$ [00-eng et al, 2019] 153
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nformation ´ottleneJJGck ;;8Variational ¶nformation ´ottleneJJGck [ ±lemi et al., 2016 ] ;;8Variational ¶nformation ´ottleneJJGck ·±''$ [00-eng et al, 2019] 154
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nformation ´ottleneJJGck ;;8Variational ¶nformation ´ottleneJJGck [ ±lemi et al., 2016 ] ;;8Variational ¶nformation ´ottleneJJGck ·±''$ [00-eng et al, 2019] 155
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nformation ´ottleneJJGck 156
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nformation ´ottleneJJGck ;=;HFarNiHFatNiToSnHFaQl /2.SnMKfTorRmHFatNiToSn ((+TottQlLJeSnLJeJHcPk ´;=;/2.((+µ @B@ ''*QlLJeRmNi LJet HFaQl¶° ±²³! BDB 157
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe 158
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe /2.SnstHFaSnJHcLJe 735ToNisLJe @B@ <8:HFaQlNiRmHFaSns LJet HFaQl¶ ±²³!&!!$ <8:øSnKIdLJerIGby LJet HFaQl¶ ±²³!&!!$ ''*rOjTovsPky HFaSnKId ((+TottTou ±²³" BDB 159
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe /2.SnstHFaSnJHcLJe 735ToNisLJe @B@ <8:HFaQlNiRmHFaSns LJet HFaQl¶ ±²³!&!!$ <8:øSnKIdLJerIGby LJet HFaQl¶ ±²³!&!!$ ''*rOjTovsPky HFaSnKId ((+TottTou ±²³" BDB 160
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe /2.SnstHFaSnJHcLJe 735ToNisLJe @B@ <8:HFaQlNiRmHFaSns LJet HFaQl¶ ±²³!&!!$ <8:øSnKIdLJerIGby LJet HFaQl¶ ±²³!&!!$ ''*rOjTovsPky HFaSnKId ((+TottTou ±²³" BDB 161
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe /2.SnstHFaSnJHcLJe 735ToNisLJe @B@ <8:HFaQlNiRmHFaSns LJet HFaQl¶ ±²³!&!!$ <8:øSnKIdLJerIGby LJet HFaQl¶ ±²³!&!!$ ''*rOjTovsPky HFaSnKId ((+TottTou ±²³" BDB 162
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe /2.SnstHFaSnJHcLJe 735ToNisLJe @B@ <8:HFaQlNiRmHFaSns LJet HFaQl¶ ±²³!&!!$ <8:øSnKIdLJerIGby LJet HFaQl¶ ±²³!&!!$ ''*rOjTovsPky HFaSnKId ((+TottTou ±²³" BDB 163
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe ;=;HFarNiHFatNiToSnHFaQl /2.SnMKfTorRmHFatNiToSn ((+TottQlLJeSnLJeJHcPk @B@ ''*QlLJeRmNi LJet HFaQl¶° ±²³! BDB 164
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe ;=;HFarNiHFatNiToSnHFaQl /2.SnMKfTorRmHFatNiToSn ((+TottQlLJeSnLJeJHcPk @B@ ''*QlLJeRmNi LJet HFaQl¶° ±²³! BDB 165
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe ;=;HFarNiHFatNiToSnHFaQl /2.SnMKfTorRmHFatNiToSn ((+TottQlLJeSnLJeJHcPk @B@ ''*QlLJeRmNi LJet HFaQl¶° ±²³! BDB 166
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational ¶nformation ´ottleneJJGck ;79LJeHFaQl ,/HFaPkLJe ;=;HFarNiHFatNiToSnHFaQl /2.SnMKfTorRmHFatNiToSn ((+TottQlLJeSnLJeJHcPk @B@ ''*QlLJeRmNi LJet HFaQl¶° ±²³! BDB 167
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ Ŷ ³reative ³onditional µ´$Ns ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 168
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 169 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 170 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 171 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 172 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 173 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 174 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 175 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 176 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 177 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 178 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 179 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 180 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 181 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 182 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 183 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 184 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 185 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 186 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 187 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 188 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 189 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 190 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ²onditional ·±''$s / pix2pix 191 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s pix2pix impaJJGct among artists 192 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s pix2pix for mediJJGcal imaging 193 441Slide: 00-hillip ¶sola
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Video2;;8Video (''$;;8V¶µ¶±) 194
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¸veryIIFbody µanJJGce ''$ow 195
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ''$;;8V¶µ¶± ·au·±''$: sketJJGch->photorealistiJJGc image 196
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s !%%earning to paint (·±''$s + 330R!%%) https://learning-to-paint.githuIIFb.io/ 197
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s Ʋ ³´$Ns and 0Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 198
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$s for unsupervised feature learning ■ ¶nfo·±''$ (¶nformation &&aximizing ·±''$) ■ ´i·±''$ (´idireJJGctional ·enerative ±dversarial ''$etworks) ■ ´ig´i·±''$ (´ig ´idireJJGctional ·enerative ±dversarial ''$etworks) 199
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 200
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 201
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 202
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 203
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 204
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 205
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 206
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 207
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 208
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 209
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶nfo·±''$ 210
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 663Unsupervised ²ategory µisJJGcovery - ´ig·±''$ 211 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 663Unsupervised ²ategory µisJJGcovery - ´ig·±''$ 212
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig ´idireJJGctional ·±''$ 213 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$:663UnJJGconditional ¶mage ·eneration 214 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$ - 663UnJJGconditional ¶mage ·eneration 215 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$ 216 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$ 217 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$ 218 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$ 219 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$: 330Representation !%%earning 220 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$: !%%atent 441SpaJJGce ''$''$s 221 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$ 330ReJJGconstruJJGctions 222 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ´ig´i·±''$ 330ReJJGconstruJJGctions 223 441Slide: °eff µonahue
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations Ʋ ³´$Ns as µnergy #Models ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 224
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¸nergy &&odels ■ ±ssign energy E(x) to every possiIIFble x ■ !%%ow energy makes for high proIIFbaIIFbility x ■ ³igh energy makes for low proIIFbaIIFbility x ■ 00-raJJGctiJJGcal JJGchallenge: domain of x usually very large ■ ¸.g. all possiIIFble 28x28x3 images = 256^(28*28*3) ~ 10^16464 225
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¸nergy &&odels 226
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¸nergy &&odels -- µefinition ■ ±ssign energy E(x) to every possiIIFble x ■ !%%ow energy makes for high proIIFbaIIFbility x ■ ³igh energy makes for low proIIFbaIIFbility x ■ 00-raJJGctiJJGcal JJGchallenge: domain of x usually very large ■ ¸.g. all possiIIFble 28x28x3 images = 256^(28*28*3) ~ 10^16464 Ŷ Z impractical to compute 227
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¸nergy &&odels -- &&aximum !%%ikelihood ²an we just ignore Z and maximize first part of the oIIFbjeJJGctive? ... 228
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8Variational !%%ower ´ound for log Z 229
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¸nergy &&odels -- &&aximum !%%ikelihood ... = entropy regularized ·±''$ oIIFbjeJJGctive 230
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$s and ¸nergy &&odels - ·±''$ with ¸ntropy 330Regularization = ¸nergy &&odel with disJJGcriminator JJGcomputing the energy - 441SpeJJGcifiJJGc parameterizations of ¸ JJGcan lead to different ·±''$ models ? ¶s ¸ntropy easy to JJGcompute - ·enerally, no … IIFbut JJGcan IIFbe done for some models, worth investigating! 231
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$s and ¸nergy &&odels ¶nspiring, JJGclean JJGcomprehensive mathematiJJGcal write-up - °ohn 441SJJGchulman ''$otes ))&n ·±''$s, ¸nergy-´ased &&odels, and 441Saddle 00-oints http://josJJGchu.net/doJJGcs/gan-notes.pdf (2016) ¸arly papers: - 552Taesup $$im and >>;Yoshua ´engio µeep µireJJGcted ·enerative &&odels with ¸nergy-´ased 00-roIIFbaIIFbility ¸stimation https://arxiv.org/aIIFbs/1606.03439 - °unIIFbo Zhao, &&iJJGchael &&athieu, >>;Yann !%%e²un ¸nergy-IIFbased ·enerative ±dversarial ''$etwork https://arxiv.org/aIIFbs/1609.03126 330ReJJGcent related papers: - ±dji ´. µieng, ¹ranJJGcisJJGco °. 330R. 330Ruiz, µavid &&. ´lei, &&iJJGchalis $$. 552Titsias 00-resJJGcriIIFbed ·enerative ±dversarial ''$etworks https://arxiv.org/aIIFbs/1910.04302 - ±ditya ·rover, &&anik µhar, 441Stefano ¸rmon ¹low-·±''$: ²omIIFbining &&aximum !%%ikelihood and ±dversarial !%%earning in ·enerative &&odels https://arxiv.org/aIIFbs/1705.08868 232
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels Ŷ µ´$Ns and Optimal 2Transport, ¶mplicit !"Likelihood #Models, #Moment #Matching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 233
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 330ReJJGcall: <<9Wasserstein µistanJJGce ■ ¸arth &&over (¸&&) distanJJGce ■ <<9W-·±''$ optimizes the dual ■ ³ow aIIFbout optimizing the primal direJJGctly? → ))&ptimal 552Transport ·±''$s 234
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$s and ))&ptimal 552Transport ■ ¶mproving ·±''$s using ))&ptimal 552Transport 552Tim 441Salimans, ³an Zhang, ±leJJGc 330Radford, µimitris &&etaxas ¶²!%%330R 2018 https://arxiv.org/aIIFbs/1803.05573 ■ 441Sinkhorn ±utoµiff ·±''$: !%%earning ·enerative &&odels with 441Sinkhorn µivergenJJGces ±ude ·enevay, ·aIIFbriel 00-eyre, &&arJJGco ²uturi ±¶441S552T±552T441S 2018 https://arxiv.org/aIIFbs/1706.00292 ■ ²ramer ·±''$: 552The ²ramer µistanJJGce as a 441Solution to ´iased <<9Wasserstein ·radients &&arJJGc ´ellemare, ¶vo µanihelka, <<9W µaIIFbney, 441S &&ohamed, ´ !%%askhiminarayanan, 441S ³oyer, 330R &&unos https://arxiv.org/aIIFbs/1705.10743 235
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ¶mpliJJGcit !%%ikelihood &&odels · /2.RmpQlNiJHcNit 62HFaxNiRmuRm 51NiPkLJeQlNiMhToToKId ++.stNiRmHFatNiToSn 40LJe 51Ni° 3/NitLJeSnKIdrHFa 62HFaQlNiPk Mhttps% ¸¸HFarxNiv¶TorLg¸HFaIGbs¸³#²$¶²$²#" · **-NivLJersLJe /2.RmHFaLgLJe <8:ySntMhLJesNis MKfrToRm <8:LJeRmHFaSntNiJHc 51HFayTouts vNiHFa )),ToSnKIdNitNiToSnHFaQl /2.6251++. 40LJe 51Ni° 9;9NiHFaSnMhHFaTo ?A?MhHFaSnLg° 3/NitLJeSnKIdrHFa 62HFaQlNiPk Mhttps% ¸¸HFarxNiv¶TorLg¸HFaIGbs¸³#³³¶³±¹"¹ → LJevLJery trHFaNiSnNiSnLg KIdHFatHFa pToNiSnt SnLJeLJeKIds tTo MhHFavLJe HFa JHcQlTosLJeIGby LgLJeSnLJerHFatLJeKId SnLJeNiLgMhIGbTor 236
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s &&oment &&atJJGching ■ ey idea : &&atJJGch the moments of the data and model distriIIFbutions to IIFbring them JJGcloser ■ ²alled the two-sample test in hypothesis testing ■ ''$ot feasiIIFble to JJGcompute higher order moments in high dimensions 237
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s &&oment &&atJJGching ■ ernel triJJGck 238
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s &&oment &&atJJGching ■ ■ ■ ■ 330Refer to ·retton et al 2007, 2012 - &&athematiJJGcal 552Treatment 239
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·enerative &&oment &&atJJGching ''$etworks 240 !%%i, 441Swersky, Zemel (2015)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·enerative &&oment &&atJJGching ''$etworks 241 !%%i, 441Swersky, Zemel (2015) !%%i, 441Swersky, Zemel (2015)
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·enerative &&oment &&atJJGching ''$etworks ■ ''$eed a good kernel for the mean disJJGcrepanJJGcy measure ■ ''$ot shown to sJJGcale well IIFbeyond &&''$¶441S552T and 552T¹µ (and some variants on ²¶¹±330R 10 later) - needs autoenJJGcoding, large miniIIFbatJJGch and mixture of kernels with different IIFbandwidths 242
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching Ʋ Other uses of ´dversarial !"Loss: 2Transfer !"Learning, ¶airness ■ ·±''$s and ¶mitation !%%earning 243
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&ther 663Uses of ±dversarial !%%oss -- 552Transfer - µeep µomain ²onfusion: &&aximizing for µomain ¶nvarianJJGce ¸riJJGc 552Tzeng, °udy ³offman, ''$ing Zhang, $$ate 441Saenko, 552Trevor µarrell https://arxiv.org/aIIFbs/1412.3474 244
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&ther 663Uses of ±dversarial !%%oss -- ¹airness ... ... ... 245
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ))&utline ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, ´ig·±''$-µeep, 441Style·±''$, 441Style·±''$-v2, ;;8V¶´-·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport, ¶mpliJJGcit !%%ikelihood &&odels, &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness Ʋ ³´$Ns and ²mitation !"Learning 246
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$s and ¶mitation !%%earning: ·±¶!%% ·±¶!%%: ·enerative ±dversarial ¶mitation !%%earning °onathan ³o, 441Stefano ¸rmon ''$eur¶00-441S 2016 https://arxiv.org/pdf/1606.03476.pdf ²mitation learning as a ³´$N proFblem: - µisJJGcriminator tries to distinguish trajeJJGctories (s,a) from demonstrator vs. from learned imitation poliJJGcy pi - !%%earned poliJJGcy pi tries to make itself indistinguishaIIFble from demonstrator - ''$ote: matJJGches ¸nergy-´ased &&odel ·±''$ formulation 247
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$s and ¶mitation !%%earning: ·±¶!%% ·±¶!%%: ·enerative ±dversarial ¶mitation !%%earning °onathan ³o, 441Stefano ¸rmon ''$eur¶00-441S 2016 https://arxiv.org/pdf/1606.03476.pdf 248
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$s and ¶mitation !%%earning: ·±¶!%% ·±¶!%%: ·enerative ±dversarial ¶mitation !%%earning °onathan ³o, 441Stefano ¸rmon ''$eur¶00-441S 2016 https://arxiv.org/pdf/1606.03476.pdf 249
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ·±''$s and ¶mitation !%%earning: ;;8V±¶!%% ;;8Variational µisJJGcriminator ´ottleneJJGck: ¶mproving ¶mitation !%%earning, ¶nverse 330R!%%, and ·±''$s IIFby ²onstraining ¶nformation ¹low ==:Xue ´in 00-eng, ±ngjoo $$anazawa, 441Sam 552Toyer, 00-ieter ±IIFbIIFbeel, 441Sergey !%%evine ¶²!%%330R 2019 https://arxiv.org/pdf/1810.00821.pdf - 330ReJJGcall: ;;8Variational µisJJGcriminator ´ottleneJJGck ·±''$ 250
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8V±¶!%% 251
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8V±¶!%% 252
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8V±¶!%% 253
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8V±¶!%% 254
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8V±¶!%% ;;8V±¶!%% [&&erel et al 2017] 255
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s ;;8V±¶!%% ;;8Variational µisJJGcriminator ´ottleneJJGck: ¶mproving ¶mitation !%%earning, ¶nverse 330R!%%, and ·±''$s IIFby ²onstraining ¶nformation ¹low ==:Xue ´in 00-eng, ±ngjoo $$anazawa, 441Sam 552Toyer, 00-ieter ±IIFbIIFbeel, 441Sergey !%%evine ¶²!%%330R 2019 https://arxiv.org/pdf/1810.00821.pdf 256
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663U² ´erkeley -- 441Spring 2020 -- µeep 663Unsupervised !%%earning -- 00-ieter ±IIFbIIFbeel, 00-eter ²hen, °onathan ³o, ±ravind 441Srinivas, ±lex !%%i, <<9Wilson >>;Yan -- !%%5 & !%%6 ¶mpliJJGcit &&odels / ·±''$s 441Summary ■ &&otivation & µefinition of ¶mpliJJGcit &&odels ■ ))&riginal ·±''$ (·oodfellow et al, 2014) ■ ¸valuation: 00-arzen, ¶nJJGception, ¹reJJGchet ■ 441Some 552Theory: ´ayes-optimal µisJJGcriminator; °ensen-441Shannon µivergenJJGce; &&ode ²ollapse; ±voiding 441Saturation ■ ·±''$ 00-rogression: ■ µ² ·±''$ (330Radford et al, 2016) ■ ¶mproved 552Training of ·±''$s (441Salimans et al, 2016) ■ <<9W·±''$, <<9W·±''$-·00-, 00-rogressive ·±''$, 441S''$-·±''$, 441S±·±''$ ■ ´ig·±''$, 441Style·±''$ ■ &&ore ·±''$s: ´ig·±''$-µeep, 441Style·±''$-v2, ;;8V¶´-·±''$, !%%))&·±''$ ■ ²reative ²onditional ·±''$s ■ ·±''$s and 330Representations ■ ·±''$s as ¸nergy &&odels ■ ·±''$s and ))&ptimal 552Transport ■ ¶mpliJJGcit !%%ikelihood &&odels ■ &&oment &&atJJGching ■ ))&ther uses of ±dversarial !%%oss: 552Transfer !%%earning, ¹airness ■ ·±''$s and ¶mitation !%%earning 257
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