Title: Variational Inference of Disentangled Latent Concepts from Unlabeled Observations,
Abstract: Disentangled representations, where the higher level data generative factors
are reflected in disjoint latent dimensions, offer several benefits such as
ease of deriving invariant representations, transferability to other tasks,
interpretability, etc. We consider the problem of unsupervised learning of
disentangled representations from large pool of unlabeled observations, and
propose a variational inference based approach to infer disentangled latent
factors. We introduce a regularizer on the expectation of the approximate
posterior over observed data that encourages the disentanglement. We also
propose a new disentanglement metric which is better aligned with the
qualitative disentanglement observed in the decoder's output. We empirically
observe significant improvement over existing methods in terms of both
disentanglement and data likelihood (reconstruction quality).