This is the official code repository for the paper "Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence" which is accepted at NeurIPS 2020.(paper link)
Still work in progress... in case of questions/problems, do not hesitate to reach out to us!
This code was developed and tested with:
- Python version 3.5.6
- PyTorch version 1.4.0
- CUDA version 11.0
- The conda environment defined in
environment.yml
First, set up the conda enviroment as follows:
conda env create -f environment.yml # create conda env conda activate mmjsd # activate conda env
Second, download the data, inception network, and pretrained classifiers:
curl -L -o tmp.zip https://drive.google.com/drive/folders/1lr-laYwjDq3AzalaIe9jN4shpt1wBsYM?usp=sharing
unzip tmp.zip
unzip celeba_data.zip -d data/
unzip data_mnistsvhntext.zip -d data/Experiments can be started by running the respective job_* script.
To choose between running the MVAE, MMVAE, and MoPoE-VAE, one needs to
change the script's METHOD variabe to "poe", "moe", or "jsd"
respectively. By default, each experiment uses METHOD="jsd".
Before running any training jobs, please make sure that you have set the paths correctly.
./job_mst
./job_celeba