Iterative Flow Matching -- Path Correction and Gradual Refinement for Enhanced Generative Modeling
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916644965580800 |
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| author | Haber, Eldad Ahamed, Shadab Siddiqui, Md. Shahriar Rahim Zakariaei, Niloufar Eliasof, Moshe |
| author_facet | Haber, Eldad Ahamed, Shadab Siddiqui, Md. Shahriar Rahim Zakariaei, Niloufar Eliasof, Moshe |
| contents | 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 $\textit{any}$ generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16445 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Iterative Flow Matching -- Path Correction and Gradual Refinement for Enhanced Generative Modeling Haber, Eldad Ahamed, Shadab Siddiqui, Md. Shahriar Rahim Zakariaei, Niloufar Eliasof, Moshe Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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 $\textit{any}$ generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems. |
| title | Iterative Flow Matching -- Path Correction and Gradual Refinement for Enhanced Generative Modeling |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.16445 |