End-to-end Differentiable Clustering with Associative Memories

Understand

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.