Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling

Abstract:Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for entertainment to solving inverse problems. Nonetheless, training a generator is a non-trivial feat that requires fine-tuning and can lead to so-called hallucinations, that is, the generation of images that are unrealistic. In this work, we explore image generation using flow matching. We explain and demonstrate why flow matching can generate hallucinations, and propose an iterative process to improve the generation process. Our iterative process can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)

arXiv:2502.16445 [cs.LG]

(or arXiv:2502.16445v4 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2502.16445 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1137/25M1736633 Focus to learn more DOI(s) linking to related resources

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