%0 Generic %1 berthelot2019remixmatch %A Berthelot, David %A Carlini, Nicholas %A Cubuk, Ekin D. %A Kurakin, Alex %A Sohn, Kihyuk %A Zhang, Han %A Raffel, Colin %D 2019 %K 2019 2020 iclr machine-learning semi-supervised %T ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring %U http://arxiv.org/abs/1911.09785 %X 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.

@misc{berthelot2019remixmatch, 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 $4{,}000$ 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.}, added-at = {2020-01-14T15:33:24.000+0100}, author = {Berthelot, David and Carlini, Nicholas and Cubuk, Ekin D. and Kurakin, Alex and Sohn, Kihyuk and Zhang, Han and Raffel, Colin}, biburl = {https://www.bibsonomy.org/bibtex/2dba24cbb53cab59608dbd9b2b460cca5/analyst}, description = {[1911.09785] ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring}, interhash = {3f9ac204260ab72855a65103eacd022c}, intrahash = {dba24cbb53cab59608dbd9b2b460cca5}, keywords = {2019 2020 iclr machine-learning semi-supervised}, note = {cite arxiv:1911.09785}, timestamp = {2020-01-14T15:33:24.000+0100}, title = {ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring}, url = {http://arxiv.org/abs/1911.09785}, year = 2019 }