  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).
