中文标题#
以中心为导向的原型对比聚类
英文标题#
Center-Oriented Prototype Contrastive Clustering
中文摘要#
对比学习由于其区分性表示而在聚类任务中被广泛使用。 然而，类间的冲突问题难以有效解决。 现有方法尝试通过原型对比来解决这个问题，但硬原型的计算与真实聚类中心之间存在偏差。 为了解决这个问题，我们提出了一种以中心为导向的原型对比聚类框架，该框架包括一个软原型对比模块和一个双一致性学习模块。 简而言之，软原型对比模块使用样本属于聚类中心的概率作为权重来计算每个类别的原型，同时避免类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和不同样本的邻域进行对齐，确保特征具有变换不变的语义信息和紧密的类内分布，同时为原型的计算提供可靠的保证。 在五个数据集上的大量实验表明，所提出的方法相比 SOTA 是有效的。 我们的代码已发布在 https://github.com/LouisDong95/CPCC。
英文摘要#
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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