Given 3 images A, B, C and their corresponding feature encodings (A), (B), (C), if the Euclidean distance () between the feature encodings is given as d((A), (B)) = 2, d((B), (C)) = 5 and d(o(A), (C)) = 6, which pair of images is the most similar? O CA

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
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Given 3 images A, B, C and their corresponding feature encodings (A), (B), (C), if the Euclidean distance
d(.) between the feature encodings is given as d(o(A), (B)) = 2, d((B), (C)) = 5 and d(o(A), (C)) = 6,
which pair of images is the most similar?
%3D
O CA
O AB
О в. с
Which of the following scenarios better describes semi-supervised learning?
O We are provided with data X and corresponding labels Y to train a classifier mapping XY.
O we are provided with unlabeled data X and we train a neural network in a supervised manner using a proxy
loss function created by modifying data X.
O We are provided with unlabeled data X and a few labeled pairs {r, y}", with which we can train a classifier
for X.
We are provided with unlabeled data X and we try to find the number of clusters in X.
Transcribed Image Text:Given 3 images A, B, C and their corresponding feature encodings (A), (B), (C), if the Euclidean distance d(.) between the feature encodings is given as d(o(A), (B)) = 2, d((B), (C)) = 5 and d(o(A), (C)) = 6, which pair of images is the most similar? %3D O CA O AB О в. с Which of the following scenarios better describes semi-supervised learning? O We are provided with data X and corresponding labels Y to train a classifier mapping XY. O we are provided with unlabeled data X and we train a neural network in a supervised manner using a proxy loss function created by modifying data X. O We are provided with unlabeled data X and a few labeled pairs {r, y}", with which we can train a classifier for X. We are provided with unlabeled data X and we try to find the number of clusters in X.
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