Milking CowMask for Semi Supervised Image Classification
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 this https URL
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