CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
This work provides a significant improvement in semi-supervised learning performance for researchers and practitioners working with limited labeled data, especially in image classification tasks.
This paper introduces CoMatch, a semi-supervised learning method that integrates class probabilities and low-dimensional embeddings. It achieves state-of-the-art results, including a 66.0% top-1 accuracy on ImageNet with 1% labels, outperforming FixMatch by 12.6%.
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.