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Despite their impressive performance on image classification tasks, deepnetworks have a hard time generalizing to unforeseen corruptions of their data.To fix this vulnerability, prior works have built complex data augmentationstrategies, combining multiple methods to enrich the training data. However,introducing intricate design choices or heuristics makes it hard to understandwhich elements of these methods are indeed crucial for improving robustness. Inthis work, we take a step back and follow a principled approach to achieverobustness to common corruptions. We propose PRIME, a general data augmentationscheme that relies on simple yet rich families of max-entropy imagetransformations. PRIME outperforms the prior art in terms of corruptionrobustness, while its simplicity and plug-and-play nature enable combinationwith other methods to further boost their robustness. We analyze PRIME to shedlight on the importance of the mixing strategy on synthesizing corruptedimages, and to reveal the robustness-accuracy trade-offs arising in the contextof common corruptions. Finally, we show that the computational efficiency ofour method allows it to be easily used in both on-line and off-line dataaugmentation schemes.

PRIME: A few primitives can boost robustness to common corruptions