Papers › Isolating Sources of Disentanglement in Variational Autoencoders

Isolating Sources of Disentanglement in Variational Autoencoders

14 Feb 2018NeurIPS 2018 12arXiv:1802.04942archive 2025-07-28

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

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 β-TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art β-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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