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Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Mubarak Shah
Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e., low speeds due to the high number of steps involved during sampling. In this survey, we provide a comprehensive review of articles on denoising diffusion models applied in vision, comprising both theoretical and practical contributions in the field. First, we identify and present three generic diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. We further discuss the relations between diffusion models and other deep generative models, including variational auto-encoders, generative adversarial networks, energy-based models, autoregressive models and normalizing flows. Then, we introduce a multi-perspective categorization of diffusion models applied in computer vision. Finally, we illustrate the current limitations of diffusion models and envision some interesting directions for future research.
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Croitoru, F.-A., Hondru, V., Ionescu, R. T., & Shah, M. (2023). Diffusion Models in Vision: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9), 10850–10869. https://doi.org/10.1109/tpami.2023.3261988
Croitoru, Florinel-Alin, et al. "Diffusion Models in Vision: A Survey." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, 2023, pp. 10850–10869, https://doi.org/10.1109/tpami.2023.3261988.
Croitoru, Florinel-Alin, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. "Diffusion Models in Vision: A Survey." IEEE Transactions on Pattern Analysis and Machine Intelligence 45, no. 9 (2023): 10850–10869. https://doi.org/10.1109/tpami.2023.3261988.
Croitoru F.-A., Hondru V., Ionescu R. T. [и др.] Diffusion Models in Vision: A Survey // IEEE Transactions on Pattern Analysis and Machine Intelligence. — 2023. — Vol. 45, № 9. — P. 10850–10869. — URL: https://doi.org/10.1109/tpami.2023.3261988
@article{croitoru2023diffusion,
title = {{Diffusion Models in Vision: A Survey}},
author = {Florinel-Alin Croitoru and Vlad Hondru and Radu Tudor Ionescu and Mubarak Shah},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2023},
volume = {45},
number = {9},
pages = {10850--10869},
doi = {10.1109/tpami.2023.3261988},
url = {https://doi.org/10.1109/tpami.2023.3261988}
}TY - JOUR AU - Croitoru, Florinel-Alin AU - Hondru, Vlad AU - Ionescu, Radu Tudor AU - Shah, Mubarak TI - Diffusion Models in Vision: A Survey JO - IEEE Transactions on Pattern Analysis and Machine Intelligence VL - 45 IS - 9 SP - 10850 EP - 10869 PY - 2023 DA - 2023/03/27 DO - 10.1109/tpami.2023.3261988 UR - https://doi.org/10.1109/tpami.2023.3261988 ER -
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