OTMatch: Improving Semi-Supervised Learning with Optimal Transport

Zhiquan Tan, Kaipeng Zheng, Weiran Huang

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Keywords: semi-supervised learning, image classification, optimal transport

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TL;DR: We Introduce OTMatch, a novel semi-supervised learning algorithm that exploits the inherent relationship between classes using optimal transport, achieving state-of-the-art on several benchmarks..

Abstract: Semi-supervised learning has made remarkable strides by effectively utilizing a limited amount of labeled data while capitalizing on the abundant information present in unlabeled data. However, current algorithms often prioritize aligning image predictions with specific classes generated through self-training techniques, thereby neglecting the inherent relationships that exist within these classes. In this paper, we present a new approach called OTMatch, which leverages semantic relationships among classes by employing an optimal transport loss function. By utilizing optimal transport, our proposed method consistently outperforms established state-of-the-art methods. Notably, we observed a substantial improvement of a certain percentage in accuracy compared to the current state-of-the-art method, FreeMatch. OTMatch achieves $3.18\%$, $3.46\%$, and $1.28\%$ error rate reduction over FreeMatch on CIFAR-10 with 1 label per class, STL-10 with 4 labels per class, and ImageNet with 100 labels per class, respectively. This demonstrates the effectiveness and superiority of our approach in harnessing semantic relationships to enhance learning performance in a semi-supervised setting.

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Submission Number: 1241