Improving Open-Set Semi-Supervised Learning with Self-Supervision
Improving Open-Set Semi-Supervised Learning with Self-Supervision
Open-set semi-supervised learning (OSSL) embodies a practical scenario within semi-supervised learning, wherein the unlabeled training set encompasses classes absent from the labeled set. Many existing OSSL methods assume that these out-of-distribution data are harmful and put effort into excluding data belonging to unknown classes from the training objective. In contrast, …