  We address the problem of unsupervised disentanglement of discrete and
continuous explanatory factors of data. We first show a simple procedure for
minimizing the total correlation of the continuous latent variables without
having to use a discriminator network or perform importance sampling, via
cascading the information flow in the $\beta$-vae framework. Furthermore, we
propose a method which avoids offloading the entire burden of jointly modeling
the continuous and discrete factors to the variational encoder by employing a
separate discrete inference procedure.
  This leads to an interesting alternating minimization problem which switches
between finding the most likely discrete configuration given the continuous
factors and updating the variational encoder based on the computed discrete
factors. Experiments show that the proposed method clearly disentangles
discrete factors and significantly outperforms current disentanglement methods
based on the disentanglement score and inference network classification score.
The source code is available at
https://github.com/snu-mllab/DisentanglementICML19.
