ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

We improve the recently-proposed "MixMatch" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels. Augmentation anchoring feeds multiple strongly augmented versions of an input into the model and encourages each output to be close to the prediction for a weakly-augmented version of the same input. To produce strong augmentations, we propose a variant of AutoAugment which learns the augmentation policy while the model is being trained. Our new algorithm, dubbed ReMixMatch, is significantly more data-efficient than prior work, requiring between 5 and 16 less data to reach the same accuracy. For example, on CIFAR-10 with 250 labeled examples we reach 93. 73\% accuracy (compared to MixMatch's accuracy of 93. 58\% with 4, 000 examples) and a median accuracy of 84. 92\% with just four labels per class. We make our code and data open-source at https: //github. com/google-research/remixmatch.