Learning From Positive and Unlabeled Data Using Observer-GAN. (arXiv:2208.12477v1 [cs.CV])

The problem of learning from positive and unlabeled data (A.K.A. PU learning)
has been studied in a binary (i.e., positive versus negative) classification
setting, where the input data consist of (1) observations from the positive
class and their corresponding labels, (2) unlabeled observations from both
positive and negative classes. Generative Adversarial Networks (GANs) have been
used to reduce the problem to the supervised setting with the advantage that
supervised learning has state-of-the-art accuracy in classification tasks. In
order to generate …

arxiv cv data gan learning positive