Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustnessDownload PDF

Published: 04 Jun 2019, Last Modified: 05 May 2023ICML Deep Phenomena 2019Readers: Everyone
Keywords: adversarial training, robustness, spatial transformations, invariance
Abstract: This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness). Evaluated on these adversarially transformed examples, we demonstrate that adding regularization on top of standard or adversarial training reduces the relative error by 20% for CIFAR10 without increasing the computational cost. This outperforms handcrafted networks that were explicitly designed to be spatial-equivariant. Furthermore, we observe for SVHN, known to have inherent variance in orientation, that robust training also improves standard accuracy on the test set.
TL;DR: for spatial transformations robust minimizer also minimizes standard accuracy; invariance-inducing regularization leads to better robustness than specialized architectures
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