  We extend Stochastic Gradient Variational Bayes to perform posterior
inference for the weights of Stick-Breaking processes. This development allows
us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian
nonparametric version of the variational autoencoder that has a latent
representation with stochastic dimensionality. We experimentally demonstrate
that the SB-VAE, and a semi-supervised variant, learn highly discriminative
latent representations that often outperform the Gaussian VAE's.
