  Deep neural networks achieve high prediction accuracy when the train and test
distributions coincide. However, in practice various types of corruptions can
deviate from this setup and performance can be heavily degraded. There have
been only a few methods to address generalization in presence of unexpected
domain shifts observed during deployment. In this paper, a
misclassification-aware Gaussian smoothing approach is presented to improve the
robustness of image classifiers against a variety of corruptions while
maintaining clean accuracy. The intuition behind our proposed
misclassification-aware objective is revealed through bounds on the local loss
deviation in the small-noise regime. When our method is coupled with additional
data augmentations, it is empirically shown to improve upon the
state-of-the-art in robustness and uncertainty calibration on several image
classification tasks.
