Title: Disentangling by Factorising,
Abstract: We define and address the problem of unsupervised learning of disentangled
representations on data generated from independent factors of variation. We
propose FactorVAE, a method that disentangles by encouraging the distribution
of representations to be factorial and hence independent across the dimensions.
We show that it improves upon $\beta$-VAE by providing a better trade-off
between disentanglement and reconstruction quality. Moreover, we highlight the
problems of a commonly used disentanglement metric and introduce a new metric
that does not suffer from them.