Title: MutexMatch: Semi-supervised Learning with Mutex-based Consistency Regularization. Abstract: The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods usually concentrate on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. Early SSL methods mostly require low-confidence samples to optimize the same loss function as high-confidence samples, but this setting might largely challenge the low-confidence samples especially at the early training stage. In this paper, we aim to utilize low-confidence samples in a novel way, which is realized by our proposed mutex-based consistency regularization, namely MutexMatch. To be specific, the high-confidence samples are required to exactly predict "What it is" by conventional True-Positive Classifier, while the low-confidence samples, for a much simpler goal, are employed to predict "What it is not" by True-Negative Classifier with ease. In this way, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data in the training stage. The proposed MutexMatch achieves superior performance on multiple benchmark datasets, i.e., CIFAR-10, CIFAR-100, SVHN, and STL-10. Particularly, our method shows further superiority under few quantities of labeled data, e.g., 91.77% accuracy with only 20 labeled data on CIFAR-10.