CoMatch: Semi-Supervised Learning With Contrastive Graph Regularization

Junnan Li, Caiming Xiong, Steven C.H. Hoi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9475-9484


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

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@InProceedings{Li_2021_ICCV, author = {Li, Junnan and Xiong, Caiming and Hoi, Steven C.H.}, title = {CoMatch: Semi-Supervised Learning With Contrastive Graph Regularization}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {9475-9484} }