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Score-based diffusion models, while achieving minimax optimality for sampling, are often hampered by slow sampling speeds due to the high computational burden of score function evaluations.
Reverse-time diffusion equation models
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Time reversal of diffusions
Haussmann, U. G. and Pardoux, E. (1986) · 1986
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Generative modeling with denoising auto-encoders and langevin sampling
Block, A., Mroueh, Y., and Rakhlin, A. (2020) · 2002
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. and Dayan, P. (2005) · 2005
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S. (2020a) · 2010
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2020c) · 2011
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A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
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Auto-encoding variational bayes
Kingma, D. P. (2013) · 2013
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Analysis and geometry of Markov diffusion operators
Bakry, D., Gentil, I., Ledoux, M., et al. (2014) · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S. (2019) · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and Van Den Berg, R. (2021) · 2021
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Diffusion schrödinger bridge with applications to score-based generative modeling
De Bortoli, V., Thornton, J., Heng, J., and Doucet, A. (2021) · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A. (2021) · 2021
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Gotta go fast when generating data with score-based models
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., and Mitliagkas, I. (2021) · 2021
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Solving inverse problems in medical imaging with score-based generative models
Song, Y., Shen, L., Xing, L., and Ermon, S. (2021) · 2021
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Score-based diffusion models for accelerated mri
Chung, H. and Ye, J. C. (2022) · 2022
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Convergence of denoising diffusion models under the manifold hypothesis
De Bortoli, V. (2022) · 2022
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Planning with diffusion for flexible behavior synthesis
Janner, M., Du, Y., Tenenbaum, J. B., and Levine, S. (2022) · 2022
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Convergence for score-based generative modeling with polynomial complexity
Lee, H., Lu, J., and Tan, Y. (2022) · 2022
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Diffusion-lm improves controllable text generation
Li, X., Thickstun, J., Gulrajani, I., Liang, P. S., and Hashimoto, T. B. (2022) · 2022
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Let us build bridges: Understanding and extending diffusion generative models
Liu, X., Wu, L., Ye, M., and Liu, Q. (2022) · 2022
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Score-based generative models detect manifolds
Pidstrigach, J. (2022) · 2022
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Imitating human behaviour with diffusion models
Pearce, T., Rashid, T., Kanervisto, A., Bignell, D., Sun, M., Georgescu, R., Macua, S. V., Tan, S. Z., Momennejad, I., Hofmann, K., et al. (2023) · 2023
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Song, Y., Dhariwal, P., Chen, M., and Sutskever, I. (2023) · 2023
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Diffusion models: A comprehensive survey of methods and applications
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., and Yang, M.-H. (2023) · 2023
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Convergence of diffusion models under the manifold hypothesis in high-dimensions
Azangulov, I., Deligiannidis, G., and Rousseau, J. (2024) · 2024
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Convergence of flow-based generative models via proximal gradient descent in wasserstein space
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al. (2022) · 2022
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Progressive distillation for fast sampling of diffusion models
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
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Diffusion policies as an expressive policy class for offline reinforcement learning
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Minimax optimality of score-based diffusion models: Beyond the density lower bound assumptions
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