Theoretical research on generative diffusion models: an overview
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910608898654208 |
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| author | Yeğin, Melike Nur Amasyalı, Mehmet Fatih |
| author_facet | Yeğin, Melike Nur Amasyalı, Mehmet Fatih |
| contents | Generative diffusion models showed high success in many fields with a powerful theoretical background. They convert the data distribution to noise and remove the noise back to obtain a similar distribution. Many existing reviews focused on the specific application areas without concentrating on the research about the algorithm. Unlike them we investigated the theoretical developments of the generative diffusion models. These approaches mainly divide into two: training-based and sampling-based. Awakening to this allowed us a clear and understandable categorization for the researchers who will make new developments in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09016 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Theoretical research on generative diffusion models: an overview Yeğin, Melike Nur Amasyalı, Mehmet Fatih Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Generative diffusion models showed high success in many fields with a powerful theoretical background. They convert the data distribution to noise and remove the noise back to obtain a similar distribution. Many existing reviews focused on the specific application areas without concentrating on the research about the algorithm. Unlike them we investigated the theoretical developments of the generative diffusion models. These approaches mainly divide into two: training-based and sampling-based. Awakening to this allowed us a clear and understandable categorization for the researchers who will make new developments in the future. |
| title | Theoretical research on generative diffusion models: an overview |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.09016 |