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arXiv · 2306.03209

End-to-end Differentiable Clustering with Associative Memories

Bishwajit Saha·Dmitry Krotov·Mohammed J. Zaki·Parikshit Ram
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Abstract

Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem. Associative Memory models or AMs are differentiable neural networks defining a recursive dynamical system, which have been integrated with various deep learning architectures. We uncover a novel connection between the AM dynamics and the inherent discrete assignment necessary in clustering to propose a novel unconstrained continuous relaxation of the discrete clustering problem, enabling end-to-end differentiable clustering with AM, dubbed ClAM. Leveraging the pattern completion ability of AMs, we further develop a novel self-supervised clustering loss. Our evaluations on varied datasets demonstrate that ClAM benefits from the self-supervision, and significantly improves upon both the traditional Lloyd's k-means algorithm, and more recent continuous clustering relaxations (by upto 60% in terms of the Silhouette Coefficient).

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Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, Parikshit Ram. 2023-06-05. End-to-end Differentiable Clustering with Associative Memories. https://arxiv.org/abs/2306.03209

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