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Diffusion or score-based models recently showed high performance in image generation.
The fréchet distance between multivariate normal distributions
Dowson, D. and Landau, B · 1982
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Grossissement d’une filtration et retournement du temps d’une diffusion
Pardoux, E · 1986
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Random Phase Textures: Theory and Synthesis
Galerne, B., Gousseau, Y., and Morel, J.-M · 2010
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Stochastic Differential Equations: An Introduction with Applications
Øksendal, B · 2010
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Static and dynamic texture mixing using optimal transport
Ferradans, S., Xia, G.-S., Peyré, G., and Aujol, J.-F · 2013
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Gans trained by a two time-scale update rule converge to a local Nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Computational optimal transport
Peyré, G. and Cuturi, M · 2019
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Applied Stochastic Differential Equations
Särkkä, S. and Solin, A · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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A wasserstein-type distance in the space of gaussian mixture models
Delon, J. and Desolneux, A · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Diffusion schrödinger bridge with applications to score-based generative modeling
De Bortoli, V., Thornton, J., Heng, J., and Doucet, A · 2021
Cited alongside, same era.
Diffusion models beat GANs on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Convergence of denoising diffusion models under the manifold hypothesis
De Bortoli, V · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
Cited alongside, same era.
How much is enough? a study on diffusion times in score-based generative models
Franzese, G., Rossi, S., Yang, L., Finamore, A., Rossi, D., Filippone, M., and Michiardi, P · 2023
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Understanding DDPM latent codes through optimal transport
Khrulkov, V., Ryzhakov, G., Chertkov, A., and Oseledets, I · 2023
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Learning mixtures of gaussians using the DDPM objective
Shah, K., Chen, S., and Klivans, A · 2023
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The hidden linear structure in score-based models and its application, 2023
Wang, B. and Vastola, J. J · 2023
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Explicit diffusion of gaussian mixture model based image priors
Zach, M., Pock, T., Kobler, E., and Chambolle, A · 2023
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Nearly $d$-linear convergence bounds for diffusion models via stochastic localization
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The flow map of the fokker–planck equation does not provide optimal transport
Lavenant, H. and Santambrogio, F · 2022
Cited alongside, same era.
Convergence of score-based generative modeling for general data distributions
Lee, H., Lu, J., and Tan, Y · 2022
Cited alongside, same era.
Score-based generative model learn manifold-like structures with constrained mixing
Wenliang, L. K. and Moran, B · 2022
Cited alongside, same era.
Stochastic interpolants: A unifying framework for flows and diffusions, 2023
Albergo, M. S., Boffi, N. M., and Vanden-Eijnden, E · 2023
Cited alongside, same era.
Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data
Chen, M., Huang, K., Zhao, T., and Wang, M
Cited in the paper.
The probability flow ODE is provably fast
Chen, S., Chewi, S., Lee, H., Li, Y., Lu, J., and Salim, A
Cited in the paper.
Benton, J., Bortoli, V. D., Doucet, A., and Deligiannidis, G · 2024
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Convergence analysis for general probability flow odes of diffusion models in wasserstein distances
Gao, X. and Zhu, L · 2024
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Convergence for score-based generative modeling with polynomial complexity
Lee, H., Lu, J., and Tan, Y · 2024
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Product of gaussian mixture diffusion models
Zach, M., Kobler, E., Chambolle, A., and Pock, T · 2024
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