SimMatch: Semi-supervised Learning with Similarity Matching

• URL: http://arxiv.org/abs/2203.06915v2
• Date: Thu, 17 Mar 2022 12:55:53 GMT
• Title: SimMatch: Semi-supervised Learning with Similarity Matching
• Authors: Mingkai Zheng, Shan You, Lang Huang, Fei Wang, Chen Qian, Chang Xu
• Abstract summary: SimMatch is a new semi-supervised learning framework that considers semantic similarity and instance similarity. With 400 epochs of training, SimMatch achieves 67.2%, and 74.4% Top-1 Accuracy with 1% and 10% labeled examples on ImageNet.
• Score: 43.61802702362675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity. In SimMatch, the consistency regularization will be applied on both semantic-level and instance-level. The different augmented views of the same instance are encouraged to have the same class prediction and similar similarity relationship respected to other instances. Next, we instantiated a labeled memory buffer to fully leverage the ground truth labels on instance-level and bridge the gaps between the semantic and instance similarities. Finally, we proposed the \textit{unfolding} and \textit{aggregation} operation which allows these two similarities be isomorphically transformed with each other. In this way, the semantic and instance pseudo-labels can be mutually propagated to generate more high-quality and reliable matching targets. Extensive experimental results demonstrate that SimMatch improves the performance of semi-supervised learning tasks across different benchmark datasets and different settings. Notably, with 400 epochs of training, SimMatch achieves 67.2\%, and 74.4\% Top-1 Accuracy with 1\% and 10\% labeled examples on ImageNet, which significantly outperforms the baseline methods and is better than previous semi-supervised learning frameworks. Code and pre-trained models are available at https://github.com/KyleZheng1997/simmatch.

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