End-to-end Differentiable Clustering with Associative Memories

• URL: http://arxiv.org/abs/2306.03209v1
• Date: Mon, 5 Jun 2023 19:34:36 GMT
• Title: End-to-end Differentiable Clustering with Associative Memories
• Authors: Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, Parikshit Ram
• Abstract summary: Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem. We propose a novel unconstrained continuous relaxation of the discrete clustering problem, enabling end-to-end differentiable clustering with AM, dubbed ClAM. 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)
• Score: 23.618514621460694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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