Deep clustering with concrete k-means

ABSTRACT W e address the problem of simultaneously learning a k -means clustering and deep feature representation from unlabelle d data, which is of interest due to the potential of deep k -means to outperform traditional two-step feature extraction and shallow-clustering strategies. W e achieve this by develop ing a gradient-estimator for the non-differentiable k -means objective via the Gumbel-Softmax reparameterisation trick. In contrast to previous attempts at deep clustering, our concr ete k -means model can be optimised with respect to the canonical k -means objective and is easily trained end-to-end without resorting to alternating optimisation. W e demonstrate the efficacy of our method on standard clustering benchmarks. Index T erms-- Deep Clustering, Unsupervised Learning, Gradient Estimator 1. INTRODUCTION Clustering is a fundamental task in unsupervised machine learning, and one with numerous applications.