Deep Amortized Clustering

By Juho Lee, Yoonho Lee, Yee Whye Teh. 2019

24 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Transformers in Vision: A Survey (2021, background): “More recently, [145] improves [144] by taking a sequential approach to cluster generation, thereby allowing assignment to a variable number of clusters.”
Meta-Learning in Neural Networks: A Survey (2020, methodology): “The methods in this group that make use of feed-forwardmodels are often known as amortized clustering [178], [179], because they amortize the typically iterative computation of clustering algorithms into the cost of training a single inference model, which subsequently performs”