Multi-facet clustering variational autoencoders

Conference item

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 latent variables, each with a Mixture-of-Gaussians prior, that learns multiple clusterings simultaneously, and is trained fully unsupervised and end-to-end. MFCVAE uses a progressively-trained ladder architecture which leads to highly stable performance. We provide novel theoretical results for optimising the ELBO analytically with respect to the categorical variational posterior distribution, correcting earlier influential theoretical work. On image benchmarks, we demonstrate that our approach separates out and clusters over different aspects of the data in a disentangled manner. We also show other advantages of our model: the compositionality of its latent space and that it provides controlled generation of samples.

Authors: Falck, F; Zhang, H; Willetts, M; Nicholson, G; Yau, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Curran Associates
• Host title: Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
• Volume: 34
• Pages: 8676-8690
• Publication date: 2022-05-01
• Acceptance date: 2021-09-28
• Event title: 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
• Event location: Virtual event
• Event website: https://nips.cc/Conferences/2021/
• Event start date: 2021-12-06
• Event end date: 2021-12-14
• ISSN: 1049-5258
• ISBN: 9781713845393
• Language: English