  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, mini-ImageNet and
Tiny-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. Our code and model weights have been released at
https://github.com/NJUyued/MutexMatch4SSL.
