  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 $5\times$ and $16\times$ 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.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning