Theoretical research on generative diffusion models: an overview
<|reference_start|>Theoretical research on generative diffusion models: an overview: 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.<|reference_end|>
@article{yeğin2024theoretical,
    title={Theoretical research on generative diffusion models: an overview},
    author={Melike Nur Yeu{g}in and Mehmet Fatih Amasyal{i}},
    journal={Neurocomputing Volume 608 , 1 December 2024, 128373},
    year={2024},
    doi={10.1016/j.neucom.2024.128373},
    archivePrefix={arXiv},
    eprint={2404.09016},
    primaryClass={cs.LG cs.AI cs.CV}
}