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arXiv · 1909.13433

Deep Amortized Clustering

Juho Lee·Yoonho Lee·Yee Whye Teh
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Abstract

We propose a deep amortized clustering (DAC), a neural architecture which learns to cluster datasets efficiently using a few forward passes. DAC implicitly learns what makes a cluster, how to group data points into clusters, and how to count the number of clusters in datasets. DAC is meta-learned using labelled datasets for training, a process distinct from traditional clustering algorithms which usually require hand-specified prior knowledge about cluster shapes/structures. We empirically show, on both synthetic and image data, that DAC can efficiently and accurately cluster new datasets coming from the same distribution used to generate training datasets.

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Juho Lee, Yoonho Lee, Yee Whye Teh. 2019-09-30. Deep Amortized Clustering. https://arxiv.org/abs/1909.13433

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