Dual-Space Contrastive Learning for Open-World Semi-Supervised Classification

Despite recent progress in semi-supervised learning (SSL), its scalability remains limited in realistic scenarios where unseen classes may appear in the unlabeled data. To address this challenge, open-world SSL (OWSSL) is proposed in recent years and attracts much attention. One core difficulty in OWSSL is to enhance the representative ability for unlabeled samples, especially for those in novel classes. More recently, several works introduce contrastive learning into OWSSL and achieve impressive performance. However, they mainly focus on conducting contrastive learning solely in either feature or prediction spaces, while ignoring the thorough exploration of information potentials in dual spaces. In this study, we propose a novel method to handle OWSSL tasks via dual-space contrastive learning (DSCL). DSCL contains two modules: intraspace contrastive learning and interspace contrastive learning. In the intraspace module, we bridge the two spaces with a learnable classifier and impose contrastive learning in the dual spaces, such that the category discriminative information could be effectively utilized to improve the representative ability. In interspace module, to further enhance the utilization of complementary information from dual spaces, we introduce neighborhood information from feature space to enhance predictive learning and meanwhile utilize the cluster structure from the prediction spaces to improve intraclass compactness of the features. Compared with state-of-the-art competitors, the proposed DSCL achieves superior performance on the popular benchmarks, i.e., CIFAR100, Imagenet100, CIFAR10, CUB-200, and Scar.