  Deep neural networks achieve high prediction accuracy when the train and test
distributions coincide. In practice though, various types of corruptions occur
which deviate from this setup and cause severe performance degradations. Few
methods have been proposed to address generalization in the presence of
unforeseen domain shifts. In particular, digital noise corruptions arise
commonly in practice during the image acquisition stage and present a
significant challenge for current methods. In this paper, we propose a diverse
Gaussian noise consistency regularization method for improving robustness of
image classifiers under a variety of corruptions while still maintaining high
clean accuracy. We derive bounds to motivate and understand the behavior of our
Gaussian noise consistency regularization using a local loss landscape
analysis. Our approach improves robustness against unforeseen noise corruptions
by 4.2-18.4% over adversarial training and other strong diverse data
augmentation baselines across several benchmarks. Furthermore, it improves
robustness and uncertainty calibration by 3.7% and 5.5%, respectively, against
all common corruptions (weather, digital, blur, noise) when combined with
state-of-the-art diverse data augmentations.
