Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
Demo
Dependency
- python=3.5
- tensorflow version = 1.4
- CUDA 8.0
- cuDNN 6.0
- Environment detail is listed in `ex.yml'
Citing this work
@inproceedings{jeongICML19,
title={
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
},
author= {Yeonwoo Jeong and Hyun Oh Song},
booktitle={International Conference on Machine Learning (ICML)},
year={2019}
}
Dataset(dSprites)
- Download from https://github.com/deepmind/dsprites-dataset
Edit path
- Edit path in 'config/path.py'
- ROOT - (directory for experiment result)
- DSPRITESPATH - (directory for downloaed dsprites)
Run model
- Dsprites_exp/CascadeVAE/main.py
- Dsprites_exp/CascadeVAE-C/main.py
Trained model
- Download from here.
- Here are trained models from 10 different random seeds.
License
MIT License