Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting
- URL: http://arxiv.org/abs/2410.02601v1
- Date: Thu, 3 Oct 2024 15:43:17 GMT
- Title: Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting
- Authors: Sergei Kholkin, Grigoriy Ksenofontov, David Li, Nikita Kornilov, Nikita Gushchin, Evgeny Burnaev, Alexander Korotin,
- Abstract summary: 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.
For the practical implementation of this procedure, it is crucial to alternate between fitting a forward and backward time diffusion at each iteration.
In our work, we show that this closely connects with the pioneer approaches for the Schr"odinger Bridge based on the Iterative Proportional Fitting (IPF) procedure.
- Score: 60.003813643818965
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Adversarial Schrödinger Bridge Matching [66.39774923893103]
Iterative Markovian Fitting (IMF) procedure alternates between Markovian and reciprocal projections of continuous-time processes.
We propose a novel Discrete-time IMF (D-IMF) procedure in which learning of processes is replaced by learning just a few transition probabilities in discrete time.
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.
arXiv Detail & Related papers (2024-05-23T11:29:33Z) - Light and Optimal Schrödinger Bridge Matching [67.93806073192938]
We propose a novel procedure to learn Schr"odinger Bridges (SB) which we call the textbf Schr"odinger bridge matching.
We show that the optimal bridge matching objective coincides with the recently discovered energy-based modeling (EBM) objectives to learn EOT/SB.
We develop a light solver (which we call LightSB-M) to implement optimal matching in practice using the mixture parameterization of the Schr"odinger potential.
arXiv Detail & Related papers (2024-02-05T17:17:57Z) - Faster Video Moment Retrieval with Point-Level Supervision [70.51822333023145]
Video Moment Retrieval (VMR) aims at retrieving the most relevant events from an untrimmed video with natural language queries.
Existing VMR methods suffer from two defects: massive expensive temporal annotations and complicated cross-modal interaction modules.
We propose a novel method termed Cheaper and Faster Moment Retrieval (CFMR)
arXiv Detail & Related papers (2023-05-23T12:53:50Z) - Diffusion Bridge Mixture Transports, Schr\"odinger Bridge Problems and
Generative Modeling [4.831663144935879]
We propose a novel sampling-based iterative algorithm, the iterated diffusion bridge mixture (IDBM) procedure, aimed at solving the dynamic Schr"odinger bridge problem.
The IDBM procedure exhibits the attractive property of realizing a valid transport between the target probability measures at each iteration.
arXiv Detail & Related papers (2023-04-03T12:13:42Z) - Robust Phi-Divergence MDPs [13.555107578858307]
We develop a novel solution framework for robust MDPs with s-rectangular ambiguity sets.
We show that the associated s-rectangular robust MDPs can be solved substantially faster than with state-of-the-art commercial solvers.
arXiv Detail & Related papers (2022-05-27T19:08:55Z) - Deep Multimodal Fusion by Channel Exchanging [87.40768169300898]
This paper proposes a parameter-free multimodal fusion framework that dynamically exchanges channels between sub-networks of different modalities.
The validity of such exchanging process is also guaranteed by sharing convolutional filters yet keeping separate BN layers across modalities, which, as an add-on benefit, allows our multimodal architecture to be almost as compact as a unimodal network.
arXiv Detail & Related papers (2020-11-10T09:53:20Z) - Understanding Nesterov's Acceleration via Proximal Point Method [52.99237600875452]
The proximal point method (PPM) is often used as a building block for designing optimization algorithms.
In this work, we use the PPM method to provide conceptually simple derivations along with convergence analyses of different versions of Nesterov's accelerated gradient method (AGM)
arXiv Detail & Related papers (2020-05-17T17:17:18Z) - State-only Imitation with Transition Dynamics Mismatch [16.934888672659824]
Imitation Learning (IL) is a popular paradigm for training agents to achieve complicated goals by leveraging expert behavior.
We present a new state-only IL algorithm in this paper.
We show that our algorithm is particularly effective when there is a transition dynamics mismatch between the expert and imitator MDPs.
arXiv Detail & Related papers (2020-02-27T02:27:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.