CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery. (arXiv:2304.06928v1 [cs.CV])

We tackle the issue of generalized category discovery (GCD). GCD considers
the open-world problem of automatically clustering a partially labelled
dataset, in which the unlabelled data contain instances from novel categories
and also the labelled classes. In this paper, we address the GCD problem
without a known category number in the unlabelled data. We propose a framework,
named CiPR, to bootstrap the representation by exploiting Cross-instance
Positive Relations for contrastive learning in the partially labelled data
which are neglected in …

arxiv bootstrap clustering data dataset discovery framework generalized instances issue novel paper positive relations representation world