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Modern deep neural networks can achieve high accuracy when the trainingdistribution and test distribution are identically distributed, but thisassumption is frequently violated in practice. When the train and testdistributions are mismatched, accuracy can plummet. Currently there are fewtechniques that improve robustness to unforeseen data shifts encountered duringdeployment. In this work, we propose a technique to improve the robustness anduncertainty estimates of image classifiers. We propose AugMix, a dataprocessing technique that is simple to implement, adds limited computationaloverhead, and helps models withstand unforeseen corruptions. AugMixsignificantly improves robustness and uncertainty measures on challenging imageclassification benchmarks, closing the gap between previous methods and thebest possible performance in some cases by more than half.

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty