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Diffusion models (DMs) have emerged as powerful generative models for solving inverse problems, offering a good approximation of prior distributions of real-world image data.
Estimation of the mean of a multivariate normal distribution
Charles M Stein · 1981
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A generalized gaussian image model for edge-preserving map estimation
Charles Bouman and Ken Sauer · 1993
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Image denoising by sparse 3-d transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Cryo-em: a unique tool for the visualization of macromolecular complexity
Eva Nogales and Sjors HW Scheres · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Ambientgan: Generative models from lossy measurements
Ashish Bora, Eric Price, and Alexandros G Dimakis · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Superresolution interferometric imaging with sparse modeling using total squared variation: application to imaging the black hole shadow
Kazuki Kuramochi, Kazunori Akiyama, Shiro Ikeda, Fumie Tazaki, Vincent L Fish, Hung-Yi Pu, Keiichi Asada, and Mareki Honma · 2018
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Machine learning: A probabilistic perspective (adaptive computation and machine learning series)
Kevin P Murphy · 2018
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Snips: Solving noisy inverse problems stochastically
Bahjat Kawar, Gregory Vaksman, and Michael Elad · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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On aliased resizing and surprising subtleties in gan evaluation
Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Solving inverse problems with score-based generative priors learned from noisy data
Asad Aali, Marius Arvinte, Sidharth Kumar, and Jonathan I Tamir · 2023
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Ambient diffusion: Learning clean distributions from corrupted data, 2023
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, and Adam Klivans · 2023
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Efficient bayesian computational imaging with a surrogate score-based prior
Berthy T Feng and Katherine L Bouman · 2023
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Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Solving inverse problems in medical imaging with score-based generative models
Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon · 2021
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Deep probabilistic imaging: Uncertainty quantification and multi-modal solution characterization for computational imaging
He Sun and Katherine L Bouman · 2021
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Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael T Mccann, Marc L Klasky, and Jong Chul Ye · 2022
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Diffusion models as plug-and-play priors
Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, and Dimitris Samaras · 2022
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Imagen video: High definition video generation with diffusion models
Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al · 2022
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Video diffusion models
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Score-based diffusion models as principled priors for inverse imaging
Berthy T Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L Bouman, and William T Freeman · 2023
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Mdm: Molecular diffusion model for 3d molecule generation
Lei Huang, Hengtong Zhang, Tingyang Xu, and Ka-Chun Wong · 2023
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Gsure-based diffusion model training with corrupted data
Bahjat Kawar, Noam Elata, Tomer Michaeli, and Michael Elad · 2023
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Ambientflow: Invertible generative models from incomplete, noisy measurements
Varun A Kelkar, Rucha Deshpande, Arindam Banerjee, and Mark A Anastasio · 2023
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A comprehensive survey on knowledge distillation of diffusion models
Weijian Luo · 2023
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Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior
Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen · 2023
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Diffusion models in bioinformatics and computational biology
Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen, Duolin Wang, Dong Xu, and Jianlin Cheng · 2024
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Understanding diffusion objectives as the elbo with simple data augmentation
Diederik Kingma and Ruiqi Gao · 2024
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