  Despite their impressive performance on image classification tasks, deep
networks have a hard time generalizing to unforeseen corruptions of their data.
To fix this vulnerability, prior works have built complex data augmentation
strategies, combining multiple methods to enrich the training data. However,
introducing intricate design choices or heuristics makes it hard to understand
which elements of these methods are indeed crucial for improving robustness. In
this work, we take a step back and follow a principled approach to achieve
robustness to common corruptions. We propose PRIME, a general data augmentation
scheme that relies on simple yet rich families of max-entropy image
transformations. PRIME outperforms the prior art in terms of corruption
robustness, while its simplicity and plug-and-play nature enable combination
with other methods to further boost their robustness. We analyze PRIME to shed
light on the importance of the mixing strategy on synthesizing corrupted
images, and to reveal the robustness-accuracy trade-offs arising in the context
of common corruptions. Finally, we show that the computational efficiency of
our method allows it to be easily used in both on-line and off-line data
augmentation schemes.
