  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. Intuitively, this regularization term
encourages perturbing the underlying source distribution to enlarge predictive
uncertainty of the current model, so that the generated "hard" adversarial
perturbations can improve the model robustness during training. Experimental
results on three standard benchmarks demonstrate that our method consistently
outperforms the existing state of the art by a statistically significant
margin.
