The Singular Values of Convolutional LayersDownload PDF

Published: 21 Dec 2018, Last Modified: 22 Oct 2023ICLR 2019 Conference Blind SubmissionReaders: Everyone
Abstract: We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation. This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball. We show that this is an effective regularizer; for example, it improves the test error of a deep residual network using batch normalization on CIFAR-10 from 6.2% to 5.3%.
Keywords: singular values, operator norm, convolutional layers, regularization
TL;DR: We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation.
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