  To improve the ability of VAE to disentangle in the latent space, existing
works mostly focus on enforcing independence among the learned latent factors.
However, the ability of these models to disentangle often decreases as the
complexity of the generative factors increases. In this paper, we investigate
the little-explored effect of the modeling capacity of a posterior density on
the disentangling ability of the VAE. We note that the independence within and
the complexity of the latent density are two different properties we constrain
when regularizing the posterior density: while the former promotes the
disentangling ability of VAE, the latter -- if overly limited -- creates an
unnecessary competition with the data reconstruction objective in VAE.
Therefore, if we preserve the independence but allow richer modeling capacity
in the posterior density, we will lift this competition and thereby allow
improved independence and data reconstruction at the same time. We investigate
this theoretical intuition with a VAE that utilizes a non-parametric latent
factor model, the Indian Buffet Process (IBP), as a latent density that is able
to grow with the complexity of the data. Across three widely-used benchmark
data sets and two clinical data sets little explored for disentangled learning,
we qualitatively and quantitatively demonstrated the improved disentangling
performance of IBP-VAE over the state of the art. In the latter two clinical
data sets riddled with complex factors of variations, we further demonstrated
that unsupervised disentangling of nuisance factors via IBP-VAE -- when
combined with a supervised objective -- can not only improve task accuracy in
comparison to relevant supervised deep architectures but also facilitate
knowledge discovery related to task decision-making. A shorter version of this
work will appear in the ICDM 2019 conference proceedings.
