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

Fanny Yang; Zuowen Wang; Christina Heinze-Deml

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arXiv · 1906.11235

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

Fanny Yang·Zuowen Wang·Christina Heinze-Deml

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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. We prove that this no-trade-off phenomenon holds for adversarial examples from transformation groups in the infinite data limit.

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Fanny Yang, Zuowen Wang, Christina Heinze-Deml. 2019-06-26. Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness. https://arxiv.org/abs/1906.11235

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