OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data. (arXiv:2107.08943v2 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2107.08943

Semi-supervised learning (SSL) is one of the most promising paradigms to
circumvent the expensive labeling cost for building a high-performance model.
Most existing SSL methods conventionally assume both labeled and unlabeled data
are drawn from the same (class) distribution. However, unlabeled data may
include out-of-class samples in practice; those that cannot have one-hot
encoded labels from a closed-set of classes in label data, i.e. unlabeled data
is an open-set. In this paper, we introduce OpenCoS, a method for handling this …

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