Clustering with Deep Learning: Taxonomy and New Methods

Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Daniel Cremers

Abstract: Clustering is a fundamental machine learning method. The quality of its results is dependent on the data distribution. For this reason, deep neural networks can be used for learning better representations of the data. In this paper, we propose a systematic taxonomy for clustering with deep learning, in addition to a review of methods from the field. Based on our taxonomy, creating new methods is more straightforward. We also propose a new approach which is built on the taxonomy and surpasses some of the limitations of some previous work. Our experimental evaluation on image datasets shows that the method approaches state-of-the-art clustering quality, and performs better in some cases.

TL;DR: Unifying framework to perform clustering using deep neural networks

Keywords: clustering, deep learning, neural networks

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:1801.07648/code)

Withdrawal: Confirmed