Leveraging Positive and Unlabeled Data for Contrastive Representation Learning

The core message of this paper is to propose a novel contrastive learning objective, called puNCE, that can effectively leverage both the available positive labeled samples and the unlabeled samples to learn useful representations in the positive unlabeled (PU) learning setting, where only a few positive labeled samples and a set of unlabeled samples are available.