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.
@article{arxiv.1906.11235,
title = {Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness},
author = {Fanny Yang and Zuowen Wang and Christina Heinze-Deml},
journal= {arXiv preprint arXiv:1906.11235},
year = {2019}
}