Learning Neural Models for End-to-End Clustering

Benjamin Bruno Meier; Ismail Elezi; Mohammadreza Amirian; Oliver Durr; Thilo Stadelmann

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arXiv · 1807.04001

Learning Neural Models for End-to-End Clustering

Benjamin Bruno Meier·Ismail Elezi·Mohammadreza Amirian·Oliver Durr·Thilo Stadelmann

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Abstract

We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a distribution over the number of clusters $k$, and for each $1 \leq k \leq k_\mathrm{max}$, a distribution over the individual cluster assignment for each data point. The network is trained in advance in a supervised fashion on separate data to learn grouping by any perceptual similarity criterion based on pairwise labels (same/different group). It can then be applied to different data containing different groups. We demonstrate promising performance on high-dimensional data like images (COIL-100) and speech (TIMIT). We call this ``learning to cluster'' and show its conceptual difference to deep metric learning, semi-supervise clustering and other related approaches while having the advantage of performing learnable clustering fully end-to-end.

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Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian, Oliver Durr, Thilo Stadelmann. 2018-07-11. Learning Neural Models for End-to-End Clustering. https://arxiv.org/abs/1807.04001

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