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Despite remarkable progress on visual recognition tasks, deep neural-netsstill struggle to generalize well when training data is scarce or highlyimbalanced, rendering them extremely vulnerable to real-world examples. In thispaper, we present a surprisingly simple yet highly effective method to mitigatethis limitation: using pure noise images as additional training data. Unlikethe common use of additive noise or adversarial noise for data augmentation, wepropose an entirely different perspective by directly training on pure randomnoise images. We present a new Distribution-Aware Routing Batch Normalizationlayer (DAR-BN), which enables training on pure noise images in addition tonatural images within the same network. This encourages generalization andsuppresses overfitting. Our proposed method significantly improves imbalancedclassification performance, obtaining state-of-the-art results on a largevariety of long-tailed image classification datasets (CIFAR-10-LT,CIFAR-100-LT, ImageNet-LT, Places-LT, and CelebA-5). Furthermore, our method isextremely simple and easy to use as a general new augmentation tool (on top ofexisting augmentations), and can be incorporated in any training scheme. Itdoes not require any specialized data generation or training procedures, thuskeeping training fast and efficient.

Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images