SP$^2$OT: Semantic-Regularized Progressive Partial Optimal Transport for Imbalanced Clustering

arXiv:2404.03446v1 Announce Type: cross
Abstract: Deep clustering, which learns representation and semantic clustering without labels information, poses a great challenge for deep learning-based approaches. Despite significant progress in recent years, most existing methods focus on uniformly distributed datasets, significantly limiting the practical applicability of their methods. In this paper, we propose a more practical problem setting named deep imbalanced clustering, where the underlying classes exhibit an imbalance distribution. To address this challenge, we introduce a novel optimal transport-based pseudo-label learning …

abstract arxiv challenge clustering cs.cv cs.lg datasets deep learning distributed focus information labels paper practical progress representation semantic transport type