@inproceedings{
gushchin2024adversarial,
title={Adversarial Schr\"odinger Bridge Matching},
author={Nikita Gushchin and Daniil Selikhanovych and Sergei Kholkin and Evgeny Burnaev and Alexander Korotin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=L3Knnigicu}
}
The Schrödinger Bridge (SB) problem offers a powerful framework for combining optimal transport and diffusion models. A promising recent approach to solve the SB problem is the Iterative Markovian Fitting (IMF) procedure, which alternates between Markovian and reciprocal projections of continuous-time stochastic processes. However, the model built by the IMF procedure has a long inference time due to using many steps of numerical solvers for stochastic differential equations. To address this limitation, we propose a novel Discrete-time IMF (D-IMF) procedure in which learning of stochastic processes is replaced by learning just a few transition probabilities in discrete time. Its great advantage is that in practice it can be naturally implemented using the Denoising Diffusion GAN (DD-GAN), an already well-established adversarial generative modeling technique. We show that our D-IMF procedure can provide the same quality of unpaired domain translation as the IMF, using only several generation steps instead of hundreds.
Adversarial Schrödinger Bridge Matching
Nikita Gushchin Daniil Selikhanovych Sergei Kholkin Evgeny Burnaev Alexander Korotin