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Diffusion models have demonstrated impressive results in both data generation and downstream tasks such as inverse problems, text-based editing, classification, and more.
Estimation of the mean of a multivariate normal distribution
Charles M Stein · 1981
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1989
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Adaptive denoising based on sure risk
Xiao-Ping Zhang and Mita D Desai · 1998
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The sure-let approach to image denoising
Thierry Blu and Florian Luisier · 2007
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Generalized SURE for exponential families: Applications to regularization
Yonina C Eldar · 2008
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Monte-carlo sure: A black-box optimization of regularization parameters for general denoising algorithms
Sathish Ramani, Thierry Blu, and Michael Unser · 2008
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 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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Accelerating magnetic resonance imaging via deep learning
Shanshan Wang, Zhenghang Su, Leslie Ying, Xi Peng, Shun Zhu, Feng Liang, Dagan Feng, and Dong Liang · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Learning a variational network for reconstruction of accelerated mri data
Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P Recht, Daniel K Sodickson, Thomas Pock, and Florian Knoll · 2018
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Deep residual learning for accelerated mri using magnitude and phase networks
Dongwook Lee, Jaejun Yoo, Sungho Tak, and Jong Chul Ye · 2018
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Noise2noise: Learning image restoration without clean data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila · 2018
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Unsupervised learning with stein’s unbiased risk estimator
Christopher A Metzler, Ali Mousavi, Reinhard Heckel, and Richard G Baraniuk · 2018
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Training deep learning based denoisers without ground truth data
Shakarim Soltanayev and Se Young Chun · 2018
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Noise2self: Blind denoising by self-supervision
Joshua Batson and Loic Royer · 2019
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Misgan: Learning from incomplete data with generative adversarial networks
Steven Cheng-Xian Li, Bo Jiang, and Benjamin Marlin · 2019
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{ \{ k } \} -space deep learning for accelerated mri
Yoseo Han, Leonard Sunwoo, and Jong Chul Ye · 2019
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Miwae: Deep generative modelling and imputation of incomplete data sets
Pierre-Alexandre Mattei and Jes Frellsen · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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fastMRI: An open dataset and benchmarks for accelerated MRI, 2019
Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, and Yvonne W. Lui · 2019
Earlier work this paper cites.
Training deep learning based image denoisers from undersampled measurements without ground truth and without image prior
Magauiya Zhussip, Shakarim Soltanayev, and Se Young Chun · 2019
Earlier work this paper cites.
Noise2inverse: Self-supervised deep convolutional denoising for tomography
Allard Adriaan Hendriksen, Daniël Maria Pelt, and K Joost Batenburg · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning
Florian Knoll, Jure Zbontar, Anuroop Sriram, Matthew J. Muckley, Mary Bruno, Aaron Defazio, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzalv, Adriana Romero, Michael Rabbat, Pascal Vincent, James Pinkerton, Duo Wang, Nafissa Yakubova, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, and Yvonne W. Lui · 2020
Cited alongside, same era.
RARE: Image reconstruction using deep priors learned without groundtruth
Jiaming Liu, Yu Sun, Cihat Eldeniz, Weijie Gan, Hongyu An, and Ulugbek S Kamilov · 2020
Cited alongside, same era.
Hyperspectral image denoising using sure-based unsupervised convolutional neural networks
Han V Nguyen, Magnus O Ulfarsson, and Johannes R Sveinsson · 2020
Solving medicine’s data bottleneck: Nightingale open science
Sendhil Mullainathan and Ziad Obermeyer · 2022
Later among the works it cites.
Fast unsupervised brain anomaly detection and segmentation with diffusion models
Walter HL Pinaya, Mark S Graham, Robert Gray, Pedro F Da Costa, Petru-Daniel Tudosiu, Paul Wright, Yee H Mah, Andrew D MacKinnon, James T Teo, Rolf Jager, et al · 2022
Later among the works it cites.
Dynamic-backbone protein-ligand structure prediction with multiscale generative diffusion models
Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F Miller III, and Anima Anandkumar · 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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Structure-based drug design with equivariant diffusion models
Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, and Bruno Correia · 2022
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Cited alongside, same era.
Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Robust compressed sensing mri with deep generative priors
Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alexandros G Dimakis, and Jon Tamir · 2021
Cited alongside, same era.
Rethinking deep image prior for denoising
Yeonsik Jo, Se Young Chun, and Jonghyun Choi · 2021
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
Grad-TTS: A diffusion probabilistic model for text-to-speech
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov · 2021
Cited alongside, same era.
Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
Cited alongside, same era.
Later among the works it cites.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
Later among the works it cites.
Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Amit H Bermano, and Daniel Cohen-Or · 2022
Later among the works it cites.
Lossy compression with Gaussian diffusion
Lucas Theis, Tim Salimans, Matthew D Hoffman, and Fabian Mentzer · 2022
Later among the works it cites.
One-dimensional deep low-rank and sparse network for accelerated mri
Zi Wang, Chen Qian, Di Guo, Hongwei Sun, Rushuai Li, Bo Zhao, and Xiaobo Qu · 2022
Later among the works it cites.
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2022
Later among the works it cites.
AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise
Julian Wyatt, Adam Leach, Sebastian M Schmon, and Chris G Willcocks · 2022
Later among the works it cites.
Measurement-conditioned denoising diffusion probabilistic model for under-sampled medical image reconstruction
Yutong Xie and Quanzheng Li · 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
Closest in time.
Synthetic ecg signal generation using probabilistic diffusion models
Edmond Adib, Amanda Fernandez, Fatemeh Afghah, and John Jeff Prevost · 2023
Closest in time.
Solving 3D inverse problems using pre-trained 2D diffusion models
Hyungjin Chung, Dohoon Ryu, Michael T McCann, Marc L Klasky, and Jong Chul Ye · 2023
Closest in time.
Masked diffusion transformer is a strong image synthesizer
Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan · 2023
Closest in time.
DiffuSeq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong · 2023
Closest in time.
Thompson sampling with diffusion generative prior
Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton, and Patrick Blöbaum · 2023
Closest in time.
Imagic: Text-based real image editing with diffusion models
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani · 2023
Closest in time.
DeScoD-ECG: Deep score-based diffusion model for ecg baseline wander and noise removal
Huayu Li, Gregory Ditzler, Janet Roveda, and Ao Li · 2023
Closest in time.
Make-A-Video: Text-to-video generation without text-video data
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman · 2023
Closest in time.
Solving inverse problems in medical imaging with score-based generative models
Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon · 2023
Closest in time.
Se(3) diffusion model with application to protein backbone generation
Jason Yim, Brian L Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola · 2023
Closest in time.
Ambient diffusion: Learning clean distributions from corrupted data
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, and Adam Klivans · 2024
Closest in time.