标题： 通过社区检测的深度聚类 显示英文标题

标题： Deep Clustering via Community Detection

摘要： 深度聚类是现代人工智能中的一个关键任务，旨在将一组数据样本划分为给定数量的同质组（即聚类）。 尽管已经提出了许多深度神经网络（DNN）主干和聚类策略来完成这项任务，并且性能不断提高，但由于缺乏准确标记的样本，深度聚类仍然非常具有挑战性。 在本文中，我们提出了一种通过社区检测进行深度聚类的新方法。 它通过检测多个社区来初始化聚类，然后通过社区合并逐步扩展聚类。 与现有的聚类策略相比，社区检测引入了聚类网络分析的新视角。 因此，它具有高伪标签纯度的固有优势，这对自监督的性能至关重要。 我们在基准图像数据集上验证了所提方法的有效性。 我们的大量实验表明，它能够有效提升最先进性能（SOTA）。 我们的消融研究也证明，新的网络视角可以有效提高社区伪标签纯度，从而提升聚类性能。 显示英文摘要

摘要： Deep clustering is an essential task in modern artificial intelligence, aiming to partition a set of data samples into a given number of homogeneous groups (i.e., clusters). Even though many Deep Neural Network (DNN) backbones and clustering strategies have been proposed for the task, achieving increasingly improved performance, deep clustering remains very challenging due to the lack of accurately labeled samples. In this paper, we propose a novel approach of deep clustering via community detection. It initializes clustering by detecting many communities, and then gradually expands clusters by community merging. Compared with the existing clustering strategies, community detection factors in the new perspective of cluster network analysis. As a result, it has the inherent benefit of high pseudo-label purity, which is critical to the performance of self-supervision. We have validated the efficacy of the proposed approach on benchmark image datasets. Our extensive experiments have shown that it can effectively improve the SOTA performance. Our ablation study also demonstrates that the new network perspective can effectively improve community pseudo-label purity, resulting in improved clustering performance.