Title: InfoCatVAE: Representation Learning with Categorical Variational Autoencoders,
Abstract: This paper describes InfoCatVAE, an extension of the variational autoencoder
that enables unsupervised disentangled representation learning. InfoCatVAE uses
multimodal distributions for the prior and the inference network and then
maximizes the evidence lower bound objective (ELBO). We connect the new ELBO
derived for our model with a natural soft clustering objective which explains
the robustness of our approach. We then adapt the InfoGANs method to our
setting in order to maximize the mutual information between the categorical
code and the generated inputs and obtain an improved model.