Towards Generalizing to Unseen Domains with Few Labels

arXiv:2403.11674v1 Announce Type: new
Abstract: We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically, our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data alongside a substantially larger pool of unlabeled data. Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting. Nevertheless, SSL methods have considerable room for performance improvement when compared to fully-supervised DG …

abstract aim arxiv challenge cs.cv data domain domains features labels pool semi-supervised type