  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
https://github.com/google-research/google-research/tree/master/milking_cowmask
