Stick-Breaking Variational Autoencoders
Abstract
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.
Cite
Text
Nalisnick and Smyth. "Stick-Breaking Variational Autoencoders." International Conference on Learning Representations, 2017.Markdown
[Nalisnick and Smyth. "Stick-Breaking Variational Autoencoders." International Conference on Learning Representations, 2017.](https://mlanthology.org/iclr/2017/nalisnick2017iclr-stick/)BibTeX
@inproceedings{nalisnick2017iclr-stick,
title = {{Stick-Breaking Variational Autoencoders}},
author = {Nalisnick, Eric T. and Smyth, Padhraic},
booktitle = {International Conference on Learning Representations},
year = {2017},
url = {https://mlanthology.org/iclr/2017/nalisnick2017iclr-stick/}
}