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Diffusion models have recently attained significant interest within the community owing to their strong performance as generative models.
POCSENSE: POCS-based reconstruction for sensitivity encoded magnetic resonance imaging
Alexei A Samsonov, Eugene G Kholmovski, Dennis L Parker, and Chris R Johnson · 2004
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Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint
Kai Tobias Block, Martin Uecker, and Jens Frahm · 2007
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Analysis of discrete and hybrid stochastic systems by nonlinear contraction theory
Quang-Cuong Pham · 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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A contraction theory approach to stochastic incremental stability
Quang-Cuong Pham, Nicolas Tabareau, and Jean-Jacques Slotine · 2009
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Image inpainting using sparsity of the transform domain
Hossein Hosseini, Neda Barzegar Marvasti, and Farrokh Marvasti · 2010
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Convex analysis and monotone operator theory in Hilbert spaces
Heinz H Bauschke, Patrick L Combettes, et al · 2011
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Projection onto convex sets super-resolution image reconstruction based on wavelet bi-cubic interpolation
Zhifei Tang, Mike Deng, Chuangbai Xiao, and Jing Yu · 2011
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What regularized auto-encoders learn from the data-generating distribution
Guillaume Alain and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 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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Projections onto convex sets super-resolution reconstruction based on point spread function estimation of low-resolution remote sensing images
Chong Fan, Chaoyun Wu, Grand Li, and Jun Ma · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Esrgan: Enhanced super-resolution generative adversarial networks
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
Cited alongside, same era.
Generative image inpainting with contextual attention
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2018
Cited alongside, same era.
fastMRI: An open dataset and benchmarks for accelerated MRI
Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, et al · 2018
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Free-form image inpainting with gated convolution
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2019
Cited alongside, same era.
Variational diffusion models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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SRDiff: Single image super-resolution with diffusion probabilistic models
Haoying Li, Yifan Yang, Meng Chang, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen · 2021
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Knowledge distillation in iterative generative models for improved sampling speed
Eric Luhman and Troy Luhman · 2021
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Sdedit: Image synthesis and editing with stochastic differential equations
Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Denoising score-matching for uncertainty quantification in inverse problems
Zaccharie Ramzi, Benjamin Remy, Francois Lanusse, Jean-Luc Starck, and Philippe Ciuciu · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
Cited alongside, same era.
WaveGrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2021
Cited alongside, same era.
ILVR: Conditioning method for denoising diffusion probabilistic models
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon · 2021
Cited alongside, same era.
Score-based diffusion models for accelerated mri
Hyungjin Chung and Jong Chul Ye · 2021
Cited alongside, same era.
Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alex Nichol · 2021
Cited alongside, same era.
Generative adversarial networks for markovian temporal dynamics: Stochastic continuous data generation
Sung Woo Park, Dong Wook Shu, and Junseok Kwon · 2021
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Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2021
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UNIT-DDPM: Unpaired image translation with denoising diffusion probabilistic models
Hiroshi Sasaki, Chris G Willcocks, and Toby P Breckon · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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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 · 2021
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Learning to efficiently sample from diffusion probabilistic models
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan · 2021
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Solving inverse problems in medical imaging with score-based generative models
Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon · 2022
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