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

• URL: http://arxiv.org/abs/2404.03446v1
• Date: Thu, 4 Apr 2024 13:46:52 GMT
• Title: SP$^2$OT: Semantic-Regularized Progressive Partial Optimal Transport for Imbalanced Clustering
• Authors: Chuyu Zhang, Hui Ren, Xuming He,
• Abstract summary: We introduce a novel optimal transport-based pseudo-label learning framework. Our framework formulates pseudo-label generation as a Semantic-regularized Progressive Partial Optimal Transport problem. We employ the strategy of majorization to reformulate the SP$2$OT problem into a Progressive Partial Optimal Transport problem.
• Score: 14.880015659013681
• License: http://creativecommons.org/licenses/by/4.0/
• 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 framework. Our framework formulates pseudo-label generation as a Semantic-regularized Progressive Partial Optimal Transport (SP$^2$OT) problem, which progressively transports each sample to imbalanced clusters under several prior distribution and semantic relation constraints, thus generating high-quality and imbalance-aware pseudo-labels. To solve SP$^2$OT, we develop a Majorization-Minimization-based optimization algorithm. To be more precise, we employ the strategy of majorization to reformulate the SP$^2$OT problem into a Progressive Partial Optimal Transport problem, which can be transformed into an unbalanced optimal transport problem with augmented constraints and can be solved efficiently by a fast matrix scaling algorithm. Experiments on various datasets, including a human-curated long-tailed CIFAR100, challenging ImageNet-R, and large-scale subsets of fine-grained iNaturalist2018 datasets, demonstrate the superiority of our method.

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