Boosting Semi-Supervised Learning with Contrastive Complementary Labeling

• URL: http://arxiv.org/abs/2212.06643v1
• Date: Tue, 13 Dec 2022 15:25:49 GMT
• Title: Boosting Semi-Supervised Learning with Contrastive Complementary Labeling
• Authors: Qinyi Deng, Yong Guo, Zhibang Yang, Haolin Pan, Jian Chen
• Abstract summary: A popular approach is pseudo-labeling that generates pseudo labels only for those unlabeled data with high-confidence predictions. We highlight that data with low-confidence pseudo labels can be still beneficial to the training process. Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs.
• Score: 11.851898765002334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised learning (SSL) has achieved great success in leveraging a large amount of unlabeled data to learn a promising classifier. A popular approach is pseudo-labeling that generates pseudo labels only for those unlabeled data with high-confidence predictions. As for the low-confidence ones, existing methods often simply discard them because these unreliable pseudo labels may mislead the model. Nevertheless, we highlight that these data with low-confidence pseudo labels can be still beneficial to the training process. Specifically, although the class with the highest probability in the prediction is unreliable, we can assume that this sample is very unlikely to belong to the classes with the lowest probabilities. In this way, these data can be also very informative if we can effectively exploit these complementary labels, i.e., the classes that a sample does not belong to. Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the performance on top of existing methods. More critically, our CCL is particularly effective under the label-scarce settings. For example, we yield an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.

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