Boosting semi-supervised learning with Contrastive Complementary Labeling

Abstract

Semi-supervised learning (SSL) approaches have achieved great success in leveraging a large amount of unlabeled data to learn deep models. Among them, one popular approach is pseudo-labeling which generates pseudo labels only for those unlabeled data with high-confidence predictions. As for the low-confidence ones, existing methods often simply discard them because these unreliable pseudo labels may mislead the model. Unlike existing methods, we highlight that these low-confidence data can be still beneficial to the training process. Specifically, although we cannot determine which class a low-confidence sample belongs to, we can assume that this sample should be very unlikely to belong to those classes with the lowest probabilities (often called complementary classes/labels). Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the performance on top of existing advanced methods and is particularly effective under the label-scarce settings. For example, CCL yields an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.