Related papers: Contrastive Hierarchical Clustering

Contrastive Hierarchical Clustering

URL: http://arxiv.org/abs/2303.03389v2
Date: Wed, 21 Jun 2023 16:37:11 GMT
Title: Contrastive Hierarchical Clustering
Authors: Micha{\l} Znale\'zniak, Przemys{\l}aw Rola, Patryk Kaszuba, Jacek Tabor, Marek \'Smieja
Abstract summary: CoHiClust is a Contrastive Hierarchical Clustering model based on deep neural networks. By employing a self-supervised learning approach, CoHiClust distills the base network into a binary tree without access to any labeled data.
Score: 8.068701201341065
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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