Title: Disentangling in Variational Autoencoders with Natural Clustering,
Abstract: Learning representations that disentangle the underlying factors of
variability in data is an intuitive precursor to AI with human-like reasoning.
Consequently, it has been the object of many efforts of the machine learning
community. This work takes a step further in this direction by addressing the
scenario where generative factors present a multimodal distribution due to the
existence of class distinction in the data. We formulate a lower bound on the
joint distribution of inputs and class labels and present N-VAE, a model which
is capable of separating factors of variation which are exclusive to certain
classes from factors that are shared among classes. This model implements the
natural clustering prior through the use of a class-conditioned latent space
and a shared latent space. We show its usefulness for detecting and
disentangling class-dependent generative factors as well as for generating rich
artificial samples.