Hans Föllmer. Time reversal on wiener space. In Stochastic processes—mathematics and physics, pp. 119–129. Springer, 1986.
Giulio Franzese, Simone Rossi, Lixuan Yang, Alessandro Finamore, Dario Rossi, Maurizio Filippone, and Pietro Michiardi. How much is enough? a study on diffusion times in score-based generative models. arXiv preprint arXiv:2206.05173, 2022.
H. Föllmer and A. Wakolbinger. Time reversal of infinite-dimensional diffusions. Stochastic Processes and their Applications, 22(1):59-77, 1986. ISSN 0304-4149. doi: https://doi.org/10.1016/0304-4149(86)90114-6. URL https://www.sciencedirect.com/science/article/pii/0304414986901146.
István Gyöngy. Lattice approximations for stochastic quasi-linear parabolic partial differential equations driven by space-time white noise i. Potential Analysis, 9(1):1-25, 1998.
István Gyöngy. Lattice approximations for stochastic quasi-linear parabolic partial differential equations driven by space-time white noise ii. Potential Analysis, 11(1):1-37, 1999.
Paul Hagemann, Sophie Mildenberger, Lars Ruthotto, Gabriele Steidl, and Nicole Tianjiao Yang. Multilevel diffusion: Infinite dimensional score-based diffusion models for image generation, 2023.
Erika Hausenblas. Approximation for semilinear stochastic evolution equations. Potential Analysis, 18(2):141-186, 2003a.
Erika Hausenblas. Weak approximation for semilinear stochastic evolution equations. In Stochastic analysis and related topics VIII, pp. 111-128. Springer, 2003b.
Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan, and Qifeng Chen. Latent video diffusion models for high-fidelity video generation with arbitrary lengths. arXiv preprint arXiv:2211.13221, 2022.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, pp. 6629-6640, 2017.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (eds.), Advances in Neural Information Processing Systems, volume 33, pp. 6840-6851. Curran Associates, Inc., 2020.
Jonathan Ho, Chitwan Sahara, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation, 2021. URL https://arxiv.org/abs/2106.15282.
Jonathan Ho, William Chan, Chitwan Sahara, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, and Tim Salimans. Imagen video: High definition video generation with diffusion models, 2022. URL https://arxiv.org/abs/2210.02303.
Emiel Hoogeboom, Víctor Garcia Satorras, Clément Vignac, and Max Welling. Equivariant diffusion for molecule generation in 3D. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (eds.), Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pp. 8867-8887. PMLR, 17-23 Jul 2022.
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (eds.), Advances in Neural Information Processing Systems, 2022.
Gavin Kerrigan, Justin Ley, and Padhraic Smyth. Diffusion generative models in infinite dimensions, 2022. URL https://arxiv.org/abs/2212.00886.
Chaewon Kim, Jaeho Lee, and Jinwoo Shin. Zero-shot blind image denoising via implicit neural representations. arXiv preprint arXiv:2204.02405, 2022a.