Corpus ID: 235377159

Multi-Facet Clustering Variational Autoencoders

@article{Falck2021MultiFacetCV,
  title={Multi-Facet Clustering Variational Autoencoders},
  author={Fabian Falck and Haoting Zhang and Matthew Willetts and George Nicholson and Christopher Yau and Christopher C. Holmes},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.05241}
}
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 variational autoencoders with a hierarchy of… Expand
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