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Isolating Sources of Disentanglement in Variational Autoencoders
Tian Qi Chen · Xuechen (Chen) Li · Roger Grosse · David Duvenaud

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 210 #58

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 the beta-TCVAE (Total Correlation Variational Autoencoder) algorithm, a refinement and plug-in replacement of the beta-VAE 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 model is trained using our framework.

Author Information

Tian Qi Chen (University of Toronto)
Xuechen (Chen) Li (University of Toronto)
Roger Grosse (University of Toronto)
David Duvenaud (University of Toronto)

David Duvenaud is an assistant professor in computer science at the University of Toronto. His research focuses on continuous-time models, latent-variable models, and deep learning. His postdoc was done at Harvard University, and his Ph.D. at the University of Cambridge. David also co-founded Invenia, an energy forecasting and trading company.

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