Multi-Facet Clustering Variational Autoencoders

Published: Jun 9, 2021
Abstract
Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one could cluster over. For example, images of objects against a background could be clustered over the shape of the object and separately by the colour of the background. In this paper, we introduce Multi-Facet Clustering Variational Autoencoders (MFCVAE), a novel class of...
Paper Details
Title
Multi-Facet Clustering Variational Autoencoders
Published Date
Jun 9, 2021
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