Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups. Although recent methods achieve an extremely high similarity with the ground truth on popular benchmarks, the information contained in the flat partition is limited. In this paper, we introduce CoHiClust, a Contrastive Hierarchical Clustering model based on deep neural networks, which can be applied to typical image data. By employing a self-supervised learning approach, CoHiClust distills the base network into a binary tree without access to any labeled data. The hierarchical clustering structure can be used to analyze the relationship between clusters, as well as to measure the similarity between data points. Experiments demonstrate that CoHiClust generates a reasonable structure of clusters, which is consistent with our intuition and image semantics. Moreover, it obtains superior clustering accuracy on most of the image datasets compared to the state-of-the-art flat clustering models.

翻译：深度聚类长期以来由扁平模型主导，这类模型将数据集划分为预设数量的组别。尽管现有方法在主流基准测试中与真实标签的相似度极高，但扁平划分所包含的信息十分有限。本文提出CoHiClust——一种基于深度神经网络的对比层次聚类模型，可应用于典型图像数据。通过采用自监督学习方法，CoHiClust在无需任何标注数据的情况下将基础网络蒸馏为二叉决策树。该层次聚类结构既能用于分析簇间关系，也可衡量数据点之间的相似度。实验表明，CoHiClust能生成符合直觉与图像语义的合理簇结构，并在多数图像数据集上获得了优于现有最优扁平聚类模型的聚类准确率。