Understanding disentangling in β-VAE


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.

2017 NIPS Workshop on Learning Disentangled Representations
Loic Matthey
Loic Matthey
Staff Research Scientist in Machine Learning

ex-Neuroscientist working on Artificial General Intelligence at Google DeepMind. Unsupervised learning, structured generative models, concepts and how to make AI actually generalize is what I do.