OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data. (arXiv:2107.08943v3 [cs.CV] UPDATED)

Semi-supervised learning (SSL) has been a powerful strategy to incorporate
few labels in learning better representations. In this paper, we focus on a
practical scenario that one aims to apply SSL when unlabeled data may contain
out-of-class samples - those that cannot have one-hot encoded labels from a
closed-set of classes in label data, i.e., the unlabeled data is an open-set.
Specifically, we introduce OpenCoS, a simple framework for handling this
realistic semi-supervised learning scenario based upon a recent framework …

arxiv cv data learning semi-supervised semi-supervised learning set supervised learning