  Invariance to a broad array of image corruptions, such as warping, noise, or
color shifts, is an important aspect of building robust models in computer
vision. Recently, several new data augmentations have been proposed that
significantly improve performance on ImageNet-C, a benchmark of such
corruptions. However, there is still a lack of basic understanding on the
relationship between data augmentations and test-time corruptions. To this end,
we develop a feature space for image transforms, and then use a new measure in
this space between augmentations and corruptions called the Minimal Sample
Distance to demonstrate there is a strong correlation between similarity and
performance. We then investigate recent data augmentations and observe a
significant degradation in corruption robustness when the test-time corruptions
are sampled to be perceptually dissimilar from ImageNet-C in this feature
space. Our results suggest that test error can be improved by training on
perceptually similar augmentations, and data augmentations may not generalize
well beyond the existing benchmark. We hope our results and tools will allow
for more robust progress towards improving robustness to image corruptions.
