Papers › Understanding disentangling in β-VAE

Understanding disentangling in β-VAE

10 Apr 2018arXiv:1804.03599archive 2025-07-28

Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner

We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we show the circumstances under which representations aligned with the underlying generative factors of variation of data emerge when optimising the modified ELBO bound in β-VAE, as training progresses. From these insights, we propose a modification to the training regime of β-VAE, that progressively increases the information capacity of the latent code during training. This modification facilitates the robust learning of disentangled representations in β-VAE, without the previous trade-off in reconstruction accuracy.

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