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Score-based diffusion models, which generate new data by learning to reverse a diffusion process that perturbs data from the target distribution into noise, have achieved remarkable success across various generative tasks.
Reverse-time diffusion equation models
Anderson, B. D. (1982) · 1982
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Time reversal of diffusions
Haussmann, U. G. and Pardoux, E. (1986) · 1986
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Adaptive estimation of a quadratic functional by model selection
Laurent, B. and Massart, P. (2000) · 2000
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Natural image statistics and neural representation
Simoncelli, E. P. and Olshausen, B. A. (2001) · 2001
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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. (2005) · 2005
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Some extensions of score matching
Hyvärinen, A. (2007) · 2007
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S. (2020) · 2010
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A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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High-dimensional probability: An introduction with applications in data science
Vershynin, R. (2018) · 2018
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S. (2019) · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Wainwright, M. J. (2019) · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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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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DiffWave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B. (2021) · 2021
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The intrinsic dimension of images and its impact on learning
Pope, P., Zhu, C., Abdelkader, A., Goldblum, M., and Goldstein, T. (2021) · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2021) · 2021
Cited alongside, same era.
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Chen, S., Chewi, S., Li, J., Li, Y., Salim, A., and Zhang, A. R. (2022) · 2022
Cited alongside, same era.
Convergence of denoising diffusion models under the manifold hypothesis
De Bortoli, V. (2022) · 2022
Cited alongside, same era.
Convergence of score-based generative modeling for general data distributions
Lee, H., Lu, J., and Tan, Y. (2023) · 2023
Later among the works it cites.
Towards non-asymptotic convergence for diffusion-based generative models
Li, G., Wei, Y., Chen, Y., and Chi, Y. (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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Exploring low-dimensional subspaces in diffusion models for controllable image editing
Chen, S., Zhang, H., Guo, M., Lu, Y., Wang, P., and Qu, Q. (2024) · 2024
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Convergence analysis for general probability flow odes of diffusion models in wasserstein distances
Gao, X. and Zhu, L. (2024) · 2024
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Equivariant diffusion for molecule generation in 3d
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M. (2022) · 2022
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Convergence for score-based generative modeling with polynomial complexity
Lee, H., Lu, J., and Tan, Y. (2022) · 2022
Cited alongside, same era.
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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Hierarchical text-conditional image generation with CLIP latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022) · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022) · 2022
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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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Faster diffusion-based sampling with randomized midpoints: Sequential and parallel
Gupta, S., Cai, L., and Chen, S. (2024) · 2024
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Provable acceleration for diffusion models under minimal assumptions
Li, G. and Cai, C. (2024) · 2024
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Improved convergence rate for diffusion probabilistic models
Li, G. and Jiao, Y. (2024) · 2024
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Adapting to unknown low-dimensional structures in score-based diffusion models
Li, G. and Yan, Y. (2024) · 2024
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Non-asymptotic convergence of discrete-time diffusion models: New approach and improved rate
Liang, Y., Ju, P., Liang, Y., and Shroff, N. (2024) · 2024
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Linear convergence of diffusion models under the manifold hypothesis
Potaptchik, P., Azangulov, I., and Deligiannidis, G. (2024) · 2024
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Tang, R., Lin, L., and Yang, Y. (2024) · 2024
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Adaptivity of diffusion models to manifold structures
Tang, R. and Yang, Y. (2024) · 2024
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Score-based diffusion models via stochastic differential equations–a technical tutorial
Tang, W. and Zhao, H. (2024) · 2024
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Diffusion models learn low-dimensional distributions via subspace clustering
Wang, P., Zhang, H., Zhang, Z., Chen, S., Ma, Y., and Qu, Q. (2024) · 2024
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