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Score-based generative models (SGMs) is a recent class of deep generative models with state-of-the-art performance in many applications.
Reverse-time diffusion equation models
B. D. O. Anderson · 1982
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On choosing and bounding probability metrics
A. Gibbs and F. Su · 2002
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Weighted Csiszár-Kullback-pinsker inequalities and applications to transportation inequalities
F. Bolley and C. Villani · 2005
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Estimation of non-normalized statistical models by score matching
A. Hyvärinen and P. Dayan · 2005
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Optimal Transport: Old and New
C. Villani · 2009
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A connection between score matching and denoising autoencoders
P. Vincent · 2011
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Log-concavity and strong log-concavity: a review
A. Saumard and J. A. Wellner · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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Reflection couplings and contraction rates for diffusions
A. Eberle · 2016
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Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
A. S. Dalalyan and A. G. Karagulyan · 2019
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Generative modeling by estimating gradients of the data distribution
Y. Song and S. Ermon · 2019
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Generative modeling with denoising auto-encoders and Langevin sampling
A. Block, Y. Mroueh, and A. Rakhlin · 2020
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Improved techniques for training score-based generative models
Y. Song and S. Ermon · 2020
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Sliced score matching: A scalable approach to density and score estimation
Y. Song, S. Garg, J. Shi, and S. Ermon · 2020
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Diffusion Schrödinger bridge with applications to score-based generative modeling
V. De Bortoli, J. Thornton, J. Heng, and A. Doucet · 2021
Sqrt(d) dimension dependence of Langevin Monte Carlo
R. Li, H. Zha, and M. Tao · 2022
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Hierarchical text-conditional image generation with CLIP latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
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High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
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S. Bruno, Y. Zhang, D.-Y. Lim, Ö. D. Akyildiz, and S. Sabanis · 2023
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Time reversal of diffusion processes under a finite entropy condition
P. Cattiaux, G. Conforti, I. Gentil, and C. Léonard · 2023
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Decentralized stochastic gradient Langevin dynamics and Hamiltonian Monte Carlo
M. Gürbüzbalaban, X. Gao, Y. Hu, and L. Zhu · 2021
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Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
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Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
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Convergence of denoising diffusion models under the manifold hypothesis
V. De Bortoli · 2022
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Elucidating the design space of diffusion-based generative models
T. Karras, M. Aittala, T. Aila, and S. Laine · 2022
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Convergence for score-based generative modeling with polynomial complexity
H. Lee, J. Lu, and Y. Tan · 2022
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Diffusion models in vision: A survey
F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah · 2023
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Convergence of score-based generative modeling for general data distributions
H. Lee, J. Lu, and Y. Tan · 2023
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Diffusion models: A comprehensive survey of methods and applications
L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, Y. Shao, W. Zhang, B. Cui, and M.-H. Yang · 2023
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Nearly d d -linear convergence bounds for diffusion models via stochastic localization
J. Benton, V. De Bortoli, A. Doucet, and G. Deligiannidis · 2024
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Convergence analysis for general probability flow ODEs of diffusion models in Wasserstein distances
X. Gao and L. Zhu · 2024
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Neural network-based score estimation in diffusion models: Optimization and generalization
Y. Han, M. Razaviyayn, and R. Xu · 2024
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Contractive diffusion probabilistic models
W. Tang and H. Zhao · 2024
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