Neural networks have led to major improvements in image classification but suffer from being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution samples and their inscrutable black-box decisions. In this work we propose RATIO, a training procedure for Robustness via Adversarial Training on In- and Out-distribution, which leads to robust models with reliable and robust confidence estimates on the out-distribution. RATIO has similar generative properties to adversarial training so that visual counterfactuals produce class specific features. While adversarial training comes at the price of lower clean accuracy, RATIO achieves state-of-the-art l2-adversarial robustness on CIFAR10 and maintains better clean accuracy.
@article{arxiv.2003.09461,
title = {Adversarial Robustness on In- and Out-Distribution Improves Explainability},
author = {Maximilian Augustin and Alexander Meinke and Matthias Hein},
journal= {arXiv preprint arXiv:2003.09461},
year = {2020}
}