This is the official Python implementation of the NeurIPS 2023 paper Entropic Neural Optimal Transport via Diffusion Processes by Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry Vetrov and Evgeny Burnaev.

If you find this repository or the ideas presented in our paper useful, please consider citing our paper.

@inproceedings{
gushchin2023entropic,
title={Entropic Neural Optimal Transport via Diffusion Processes},
author={Nikita Gushchin and Alexander Kolesov and Alexander Korotin and Dmitry P. Vetrov and Evgeny Burnaev},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=fHyLsfMDIs}
}

The implementation is GPU-based with the multi-GPU support.

All the experiments are issued in the form of pretty self-explanatory jupyter notebooks (notebooks/). For convenience, the majority of the evaluation output is preserved. Auxilary source code is moved to .py modules (src/).

Note that we use wandb (link) dashboard system when launching our experiments. The practitioners are expected to use wandb too.

• notebooks/Toy_experiments.ipynb - Toy experiments.
• notebooks/High_dimensionsal_gaussians.ipynb - Experiments with high dimensional gaussians.
• stats/compute_stats.ipynb - Precomputing stats for FID evalution for colored MNIST and Celeba (you need to run it before experiments with images).
• notebooks/Image_experiments.ipynb - Training ENOT for colored MNIST and Celeba.
• notebooks/Discrete_OT.ipynb - Calculating discrete OT mappings.
• notebooks/MNIST_plotting.ipynb - Plotting ENOT and discrete OT results for colored MNIST.
• notebooks/Celeba_plotting.ipynb - Plotting ENOT results for Celeba.

• Colored MNIST. Custom dataseted obtained by coloring each MNIST digith in a random color;
• CelebA faces requires datasets/list_attr_celeba.ipynb;

The dataloaders can be created by load_dataset function from src/tools.py. The latter four datasets get loaded directly to RAM.