Bo Zhan

Rethinking Safe Semi-supervised Learning: Transferring the Open-set Problem to A Close-set One

Conventional semi-supervised learning (SSL) lies in the close-set assumption that the labeled and unlabeled sets contain data with the same seen classes, called in-distribution (ID) data. In contrast, safe SSL investigates a more challenging open-set problem where unlabeled set may involve some out-…