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.

本文提出了一种名为 AugMix 的数据处理技术，该技术对图像分类器的鲁棒性和不确定性估计有显著的提高，这有助于模型抵御在部署过程中遇到的意外破坏。