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The human visual system is remarkably robust against a wide range ofnaturally occurring variations and corruptions like rain or snow. In contrast,the performance of modern image recognition models strongly degrades whenevaluated on previously unseen corruptions. Here, we demonstrate that a simplebut properly tuned training with additive Gaussian and Speckle noisegeneralizes surprisingly well to unseen corruptions, easily reaching theprevious state of the art on the corruption benchmark ImageNet-C (withResNet50) and on MNIST-C. We build on top of these strong baseline results andshow that an adversarial training of the recognition model against uncorrelatedworst-case noise distributions leads to an additional increase in performance.This regularization can be combined with previously proposed defense methodsfor further improvement.

A simple way to make neural networks robust against diverse image corruptions