Which of the following is true for batch normalization (BatchNorm)? Mark the ones which are correct (there may be multiple correct answers; mark them all). 1. BatchNorm speeds up the training process since it mitigates the exploding gradient problem. 2. BatchNorm uses a different mean and variance for each sample at test time. 3. BatchNorm, when applied to ConvNets, computes a different mean for each neuron, based on the samples of the given minibatch. 4. BatchNorm computes a different mean and variance for each mini-batch at training time. 5. BatchNorm slows down the training process since it mitigates the vanishing gradient problem.

Which of the following is true for batch normalization (BatchNorm)? Mark the ones which are correct (there may be multiple correct answers; mark them all). 1. BatchNorm speeds up the training process since it mitigates the exploding gradient problem. 2. BatchNorm uses a different mean and variance for each sample at test time. 3. BatchNorm, when applied to ConvNets, computes a different mean for each neuron, based on the samples of the given minibatch. 4. BatchNorm computes a different mean and variance for each mini-batch at training time. 5. BatchNorm slows down the training process since it mitigates the vanishing gradient problem.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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