Code for Coupled Variational Bayes via Optimization Embedding (http://papers.nips.cc/paper/8177-coupled-variational-bayes-via-optimization-embedding.pdf)
Get the source code, and do pip install:
git clone --recursive https://github.com/Hanjun-Dai/cvb
cd cvb
pip install -e .
Download the data via the dropbox link:
https://www.dropbox.com/sh/ho3tntcr0a7jsp4/AACpmE_3ErMw2JV3y9Kr8bTXa?dl=0
And create a symbolic link to this dropbox folder. Finally the folder structure should look like this:
cvb (project root)
|__ README.md
|__ cvb
|__ dropbox -> symbolic link
|__ |__ data
| |......
|......
Directly run the script:
cd cvb/toy_img
./run_fenchel.sh
You will get a sequence of posterior samples.
There are several variants of the model.
For MNIST, we implemented cvb with gaussian, parametric optimization and also gaussian + fenchel duality;
For CelebA, we implemented cvb with parametric optimization and fenchel duality
To run the training:
cd cvb/celeb
./run_celeb_parametric_opt.sh
To visualize the image samples:
cd cvb/celeb
./vis_celeb_parametric_opt.sh
@inproceedings{dai2018coupled,
title={Coupled Variational Bayes via Optimization Embedding},
author={Dai, Bo and Dai, Hanjun and He, Niao and Liu, Weiyang and Liu, Zhen and Chen, Jianshu and Xiao, Lin and Song, Le},
booktitle={Advances in Neural Information Processing Systems},
pages={9712--9722},
year={2018}
}