Papers › Learning Disentangled Joint Continuous and Discrete Representations

Learning Disentangled Joint Continuous and Discrete Representations

31 Mar 2018NeurIPS 2018 12arXiv:1804.00104archive 2025-07-28

Emilien Dupont

We present a framework for learning disentangled and interpretable jointly continuous and discrete representations in an unsupervised manner. By augmenting the continuous latent distribution of variational autoencoders with a relaxed discrete distribution and controlling the amount of information encoded in each latent unit, we show how continuous and categorical factors of variation can be discovered automatically from data. Experiments show that the framework disentangles continuous and discrete generative factors on various datasets and outperforms current disentangling methods when a discrete generative factor is prominent.

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