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.

我们提出了一个基于最优传输的伪标签学习框架来解决深度不平衡聚类问题，该框架通过逐步将样本转移到不平衡的聚类中生成高质量且注意不平衡性的伪标签。

SP$^2$OT：语义规范化的逐步偏差最优传输用于不平衡聚类

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

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 first introduce a more
practical problem setting named deep imbalanced clustering, where the
underlying classes exhibit an imbalance distribution. To tackle this problem,
we propose a novel pseudo-labeling-based learning framework. Our framework
formulates pseudo-label generation as a progressive partial optimal transport
problem, which progressively transports each sample to imbalanced clusters
under prior distribution constraints, thus generating imbalance-aware
pseudo-labels and learning from high-confident samples. In addition, we
transform the initial formulation into an unbalanced optimal transport problem
with augmented constraints, which 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.

本文介绍了一种新的深度非平衡聚类问题，并提出了一种基于伪标签的学习框架，通过渐进式部分最优输运问题生成偏态感知的伪标签，并从高置信度样本中学习，实验证明了我们方法的优越性。