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

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

5 Dec 2019ICLR 2020 1arXiv:1912.02781archive 2025-07-28

Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.

Code

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Tasks

Results from the paper archive 2025-07-28

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Methods

Introduced by this paper: AugMix

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