用中心导向原型对比提升聚类性能，解决类别冲突与原型漂移问题。
Center-Oriented Prototype Contrastive Clustering
- 以样本归属中心的概率加权计算原型，缓解类别间冲突。
- 在5个数据集上优于当前最优方法，聚类精度显著提升。
- 适合需要高精度聚类且关注原型稳定性的研究者使用。
对比学习因具备判别性表征能力，被广泛应用于聚类任务。然而，类别间的冲突问题难以有效解决。现有方法通过原型对比尝试解决该问题，但硬原型计算与真实聚类中心存在偏差。为此，本文提出一种中心导向的原型对比聚类框架（CPCC），包含软原型对比模块和双一致性学习模块。软原型对比模块利用样本属于聚类中心的概率作为权重计算类别原型，避免类别间冲突并减少原型漂移；双一致性学习模块分别对同一样本的不同变换及不同样本的邻域进行对齐，确保特征具备变换不变语义信息和紧凑的类内分布，从而为原型计算提供可靠保障。在五个数据集上的大量实验表明，所提方法在聚类性能上优于当前最优方法。代码已开源：https://github.com/LouisDong95/CPCC。
原文摘要 · Abstract (English)
Contrastive learning is widely used in clustering tasks due to its discriminative representation. However, the conflict problem between classes is difficult to solve effectively. Existing methods try to solve this problem through prototype contrast, but there is a deviation between the calculation of hard prototypes and the true cluster center. To address this problem, we propose a center-oriented prototype contrastive clustering framework, which consists of a soft prototype contrastive module and a dual consistency learning module. In short, the soft prototype contrastive module uses the probability that the sample belongs to the cluster center as a weight to calculate the prototype of each category, while avoiding inter-class conflicts and reducing prototype drift. The dual consistency learning module aligns different transformations of the same sample and the neighborhoods of different samples respectively, ensuring that the features have transformation-invariant semantic information and compact intra-cluster distribution, while providing reliable guarantees for the calculation of prototypes. Extensive experiments on five datasets show that the proposed method is effective compared to the SOTA. Our code is published on https://github.com/LouisDong95/CPCC.
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