Awesome
MoPoE-VAE
This is the official code for the ICLR 2021 paper "Generalized Multimodal ELBO". Here is the link to the OpenReview-Site: https://openreview.net/forum?id=5Y21V0RDBV
If you have any questions about the code or the paper, we are happy to help!
Preliminaries
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 mopoe # 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/
unzip PolyMNIST.zip -d data/
Experiments
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 "joint_elbo"
respectively. By default, each experiment uses METHOD="joint_elbo"
.
running MNIST-SVHN-Text
./job_mnistsvhntext
running PolyMNIST
./job_polymnist
running Bimodal Celeba
./job_celeba