Open-Set Semi-Supervised Learning by Distribution Alignment

Semi-Supervised Learning (SSL) has been shown to be effective in the closed-set case where the label spaces in labeled and unlabeled data are the same. However, in open-set SSL, its performance is seriously degraded since unlabeled data contains some classes not seen in the labeled data, leading to the distribution mismatch between labeled and unlabeled data. To solve this problem, we propose a Distribution Aligned Openset SSL (DAOSSL) method, which aims to explicitly reduce the empirical distribution mismatch between the labeled and unlabeled data. Specifically, we first introduce a progressive separation mechanism that utilizes a coarse-to-fine pipeline to weigh the unlabeled data. Based on this weighting strategy, we then propose a weighted distribution alignment approach to minimize the distribution discrepancy between the labeled and unlabeled data. These two strategies can be easily integrated into existing deep SSL approaches for open-set SSL tasks. The effectiveness of the proposed DAOSSL method is demonstrated through empirical studies, which show that the method is able to successfully reduce the distribution mismatch between labeled and unlabeled data, resulting in performance improvement in open-set SSL tasks.