Diffusion & Adversarial Schr\"odinger Bridges via Iterative Proportional Markovian Fitting
<|reference_start|>Diffusion & Adversarial Schr\"odinger Bridges via Iterative Proportional Markovian Fitting: The Iterative Markovian Fitting (IMF) procedure based on iterative reciprocal and Markovian projections has recently been proposed as a powerful method for solving the Schr\"odinger Bridge problem. However, it has been observed that for the practical implementation of this procedure, it is crucial to alternate between fitting a forward and backward time diffusion at each iteration. Such implementation is thought to be a practical heuristic, which is required to stabilize training and obtain good results in applications such as unpaired domain translation. In our work, we show that this heuristic closely connects with the pioneer approaches for the Schr\"odinger Bridge based on the Iterative Proportional Fitting (IPF) procedure. Namely, we find that the practical implementation of IMF is, in fact, a combination of IMF and IPF procedures, and we call this combination the Iterative Proportional Markovian Fitting (IPMF) procedure. We show both theoretically and practically that this combined IPMF procedure can converge under more general settings, thus, showing that the IPMF procedure opens a door towards developing a unified framework for solving Schr\"odinger Bridge problems.<|reference_end|>
@article{kholkin2024diffusion,
    title={Diffusion & Adversarial Schr\"odinger Bridges via Iterative Proportional
  Markovian Fitting},
    author={Sergei Kholkin, Grigoriy Ksenofontov, David Li, Nikita Kornilov,
  Nikita Gushchin, Evgeny Burnaev, Alexander Korotin},
    journal={arXiv preprint arXiv:2410.02601},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02601},
    primaryClass={cs.LG}
}