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

Understand

The human visual system is remarkably robust against a wide range of naturally occurring variations and corruptions like rain or snow.

• In contrast, the performance of modern image recognition models strongly degrades when evaluated on previously unseen corruptions.
• Here, we demonstrate that a simple but properly tuned training with additive Gaussian and Speckle noise generalizes surprisingly well to unseen corruptions, easily reaching the previous state of the art on the corruption benchmark ImageNet-C (with ResNet50) and on MNIST-C.
• We build on top of these strong baseline results and show that an adversarial training of the recognition model against uncorrelated worst-case noise distributions leads to an additional increase in performance.