CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

• URL: http://arxiv.org/abs/2011.11183v2
• Date: Wed, 3 Mar 2021 01:58:15 GMT
• Title: CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
• Authors: Junnan Li, Caiming Xiong, Steven Hoi
• Abstract summary: CoMatch is a new semi-supervised learning method that unifies dominant approaches. It achieves state-of-the-art performance on multiple datasets.
• Score: 86.84486065798735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised learning has been an effective paradigm for leveraging unlabeled data to reduce the reliance on labeled data. We propose CoMatch, a new semi-supervised learning method that unifies dominant approaches and addresses their limitations. CoMatch jointly learns two representations of the training data, their class probabilities and low-dimensional embeddings. The two representations interact with each other to jointly evolve. The embeddings impose a smoothness constraint on the class probabilities to improve the pseudo-labels, whereas the pseudo-labels regularize the structure of the embeddings through graph-based contrastive learning. CoMatch achieves state-of-the-art performance on multiple datasets. It achieves substantial accuracy improvements on the label-scarce CIFAR-10 and STL-10. On ImageNet with 1% labels, CoMatch achieves a top-1 accuracy of 66.0%, outperforming FixMatch by 12.6%. Furthermore, CoMatch achieves better representation learning performance on downstream tasks, outperforming both supervised learning and self-supervised learning. Code and pre-trained models are available at https://github.com/salesforce/CoMatch.

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