MutexMatch: Semi-supervised Learning with Mutex-based Consistency Regularization

• URL: http://arxiv.org/abs/2203.14316v1
• Date: Sun, 27 Mar 2022 14:28:16 GMT
• Title: MutexMatch: Semi-supervised Learning with Mutex-based Consistency Regularization
• Authors: Yue Duan, Zhen Zhao, Lei Qi, Lei Wang, Luping Zhou, Yinghuan Shi, Yang Gao
• Abstract summary: We propose a mutex-based consistency regularization, namely Mutex, to utilize low-confidence samples in a novel way. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, and mini-ImageNet.
• Score: 36.019086181632005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this paper, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict "what it is" by conventional True-Positive Classifier, while the low-confidence samples are employed to achieve a simpler goal -- to predict with ease "what it is not" by True-Negative Classifier. In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, and mini-ImageNet. More importantly, our method further shows superiority when the amount of labeled data is scarce, e.g., 92.23% accuracy with only 20 labeled data on CIFAR-10. Code has been released at https://github.com/NJUyued/MutexMatch4SSL.

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