Milking CowMask for Semi-Supervised Image Classification

26 Mar 2020  ·  Geoff French, Avital Oliver, Tim Salimans ·

Consistency regularization is a technique for semi-supervised learning that underlies a number of strong results for classification with few labeled data. It works by encouraging a learned model to be robust to perturbations on unlabeled data. Here, we present a novel mask-based augmentation method called CowMask. Using it to provide perturbations for semi-supervised consistency regularization, we achieve a state-of-the-art result on ImageNet with 10% labeled data, with a top-5 error of 8.76% and top-1 error of 26.06%. Moreover, we do so with a method that is much simpler than many alternatives. We further investigate the behavior of CowMask for semi-supervised learning by running many smaller scale experiments on the SVHN, CIFAR-10 and CIFAR-100 data sets, where we achieve results competitive with the state of the art, indicating that CowMask is widely applicable. We open source our code at

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Semi-Supervised Image Classification cifar-100, 10000 Labels CowMix (WRN-28-96x2d) Percentage error 23.07±0.30 # 14
Semi-Supervised Image Classification ImageNet - 10% labeled data CowMix (ResNet-152) Top 5 Accuracy 91.24% # 10
Top 1 Accuracy 73.94% # 18