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arXiv · 1802.04942

Isolating Sources of Disentanglement in Variational Autoencoders

Ricky T. Q. Chen·Xuechen Li·Roger Grosse·David Duvenaud
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Abstract

We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our $\beta$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art $\beta$-VAE objective for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the latent variables model is trained using our framework.

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Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud. 2018-02-14. Isolating Sources of Disentanglement in Variational Autoencoders. https://arxiv.org/abs/1802.04942

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