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 semisupervised variant, learn highly discriminative latent representations that often outperform the Gaussian VAE's.
Nalisnick and Smyth REF proposed Stick-Breaking VAE (SB-VAE), which is a Bayesian nonparametric version of traditional VAE with a stochastic dimensionality.
Stick-Breaking Variational Autoencoders
ICLR 2017
arXiv: Machine Learning
Computer Science Mathematics