  Deep networks have achieved impressive results on a range of well-curated
benchmark datasets. Surprisingly, their performance remains sensitive to
perturbations that have little effect on human performance. In this work, we
propose a novel extension of Mixup called Robustmix that regularizes networks
to classify based on lower-frequency spatial features. We show that this type
of regularization improves robustness on a range of benchmarks such as
Imagenet-C and Stylized Imagenet. It adds little computational overhead and,
furthermore, does not require a priori knowledge of a large set of image
transformations. We find that this approach further complements recent advances
in model architecture and data augmentation, attaining a state-of-the-art mCE
of 44.8 with an EfficientNet-B8 model and RandAugment, which is a reduction of
16 mCE compared to the baseline.
