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  1. ; ; (, ICLR 2021 Workshop on Security and Safety in Machine Learning Systems)
    Chen, Xinyun; Xie, Cihang; Shafahi, Ali; Li, Bo; Zhao, Ding; Goldstein, Tom; Song, Dawn (Ed.)
    The adversarial training paradigm has become the standard in training deep neural networks for robustness. Yet, it remains unstable, with the mechanisms driving this instability poorly understood. In this study, we discover that this instability is primarily driven by a non-smooth optimization landscape and an internal covariate shift phenomenon, and show that Batch Normalization (BN) can effectively mitigate both these issues. Further, we demonstrate that BN universally improves clean and robust performance across various defenses, datasets, and model types, with greater improvement on more difficult tasks. Finally, we confirm BN’s heterogeneous distribution issue with mixed-batch training and propose a solution. 
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