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arXiv · 1804.03599

Understanding disentangling in $\beta$-VAE

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

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 $\beta$-VAE, as training progresses. From these insights, we propose a modification to the training regime of $\beta$-VAE, that progressively increases the information capacity of the latent code during training. This modification facilitates the robust learning of disentangled representations in $\beta$-VAE, without the previous trade-off in reconstruction accuracy.

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Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner. 2018-04-10. Understanding disentangling in $\beta$-VAE. https://arxiv.org/abs/1804.03599

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