  Consistency regularization on label predictions becomes a fundamental
technique in semi-supervised learning, but it still requires a large number of
training iterations for high performance. In this study, we analyze that the
consistency regularization restricts the propagation of labeling information
due to the exclusion of samples with unconfident pseudo-labels in the model
updates. Then, we propose contrastive regularization to improve both efficiency
and accuracy of the consistency regularization by well-clustered features of
unlabeled data. In specific, after strongly augmented samples are assigned to
clusters by their pseudo-labels, our contrastive regularization updates the
model so that the features with confident pseudo-labels aggregate the features
in the same cluster, while pushing away features in different clusters. As a
result, the information of confident pseudo-labels can be effectively
propagated into more unlabeled samples during training by the well-clustered
features. On benchmarks of semi-supervised learning tasks, our contrastive
regularization improves the previous consistency-based methods and achieves
state-of-the-art results, especially with fewer training iterations. Our method
also shows robust performance on open-set semi-supervised learning where
unlabeled data includes out-of-distribution samples.
