Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness

Understand

Adversarial data augmentation has shown promise for training robust deep neural networks against unforeseen data shifts or corruptions.

• However, it is difficult to define heuristics to generate effective fictitious target distributions containing "hard" adversarial perturbations that are largely different from the source distribution.
• In this paper, we propose a novel and effective regularization term for adversarial data augmentation.
• We theoretically derive it from the information bottleneck principle, which results in a maximum-entropy formulation.