ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation AnchoringDownload PDF

Sep 25, 2019 (edited Mar 11, 2020)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • Keywords: semi-supervised learning
  • TL;DR: We introduce Distribution Matching and Augmentation Anchoring, two improvements to MixMatch which produce state-of-the-art results and enable surprisingly strong performance with only 40 labels on CIFAR-10 and SVHN.
  • Abstract: 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 4000 examples) and a median accuracy of 84.92% with just four labels per class.
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