Isolating Sources of Disentanglement in Variational Autoencoders

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Authors

Ricky T. Q. Chen, Xuechen Li, Roger B. Grosse, David K. Duvenaud

Abstract

<p>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.</p>