Disentangling in Variational Autoencoders with Natural Clustering.

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.