A Closer Look at Accuracy vs. Robustness

TL;DR

This paper challenges the perceived tradeoff between robustness and accuracy in deep learning, showing that both can be achieved simultaneously on benchmark datasets by leveraging locally Lipschitz functions and improved training methods.

Contribution

It proves that robustness and accuracy are theoretically compatible for benchmark datasets and identifies practical limitations, proposing combined techniques to improve both.

Findings

Datasets are separated, enabling simultaneous robustness and accuracy.

Locally Lipschitz functions can achieve both robustness and accuracy.

Combining dropout with robust training improves generalization.

Abstract

Current methods for training robust networks lead to a drop in test accuracy, which has led prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning. We take a closer look at this phenomenon and first show that real image datasets are actually separated. With this property in mind, we then prove that robustness and accuracy should both be achievable for benchmark datasets through locally Lipschitz functions, and hence, there should be no inherent tradeoff between robustness and accuracy. Through extensive experiments with robustness methods, we argue that the gap between theory and practice arises from two limitations of current methods: either they fail to impose local Lipschitzness or they are insufficiently generalized. We explore combining dropout with robust training methods and obtain better generalization. We conclude that achieving robustness and accuracy in practice may require using methods that impose local Lipschitzness and augmenting them with deep learning generalization techniques. Code available at https://github.com/yangarbiter/robust-local-lipschitz