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Recent works have shown the surprising effectiveness of deep generative models in solving numerous image reconstruction (IR) tasks, even without training data.
Early stopping-but when?
Lutz Prechelt · 1998
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
Raymond H Chan, Chung-Wa Ho, and Mila Nikolova · 2005
Earlier work this paper cites.
Boosting with early stopping: Convergence and consistency
Tong Zhang, Bin Yu, et al · 2005
Earlier work this paper cites.
A TV based restoration model with local constraints
A. Almansa, C. Ballester, V. Caselles, and G. Haro · 2007
Earlier work this paper cites.
Introduction to the mathematics of medical imaging
Charles L Epstein · 2007
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
Earlier work this paper cites.
Proximal algorithms
Neal Parikh and Stephen Boyd · 2014
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
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Earlier work this paper cites.
Phase retrieval with application to optical imaging: a contemporary overview
Yoav Shechtman, Yonina C Eldar, Oren Cohen, Henry Nicholas Chapman, Jianwei Miao, and Mordechai Segev · 2015
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2016
Earlier work this paper cites.
Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
Earlier work this paper cites.
Phase retrieval under a generative prior
Paul Hand, Oscar Leong, and Vladislav Voroninski · 2018
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Deep decoder: Concise image representations from untrained non-convolutional networks
Reinhard Heckel and Paul Hand · 2018
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2018
Earlier work this paper cites.
Whiteness constraints in a unified variational framework for image restoration
Alessandro Lanza, Serena Morigi, Federica Sciacchitano, and Fiorella Sgallari · 2018
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Nima: Neural image assessment
Hossein Talebi and Peyman Milanfar · 2018
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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Seeing what a gan cannot generate
David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba · 2019
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A bayesian perspective on the deep image prior
Zezhou Cheng, Matheus Gadelha, Subhransu Maji, and Daniel Sheldon · 2019
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” double-dip”: Unsupervised image decomposition via coupled deep-image-priors
Yosef Gandelsman, Assaf Shocher, and Michal Irani · 2019
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Implicit rank-minimizing autoencoder
Li Jing, Jure Zbontar, et al · 2020
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Hannah Lawrence, David Bramherzig, Henry Li, Michael Eickenberg, and Marylou Gabrié · 2020
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Deep learning techniques for inverse problems in imaging
Gregory Ongie, Ajil Jalal, Christopher A Metzler, Richard G Baraniuk, Alexandros G Dimakis, and Rebecca Willett · 2020
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Exploiting deep generative prior for versatile image restoration and manipulation
Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, and Ping Luo · 2020
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Neural blind deconvolution using deep priors
Dongwei Ren, Kai Zhang, Qilong Wang, Qinghua Hu, and Wangmeng Zuo · 2020
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Denoising and regularization via exploiting the structural bias of convolutional generators
Reinhard Heckel and Mahdi Soltanolkotabi · 2019
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Algorithmic guarantees for inverse imaging with untrained network priors
Gauri Jagatap and Chinmay Hegde · 2019
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Low shot learning with untrained neural networks for imaging inverse problems
Oscar Leong and Wesam Sakla · 2019
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Audio denoising with deep network priors
Michael Michelashvili and Lior Wolf · 2019
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One-dimensional deep image prior for time series inverse problems
Sriram Ravula and Alexandros G Dimakis · 2019
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Deep geometric prior for surface reconstruction
Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, and Daniele Panozzo · 2019
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Solving inverse problems with hybrid deep image priors: the challenge of preventing overfitting
Zhaodong Sun · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng · 2020
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Inverse problems, deep learning, and symmetry breaking
Kshitij Tayal, Chieh-Hsin Lai, Vipin Kumar, and Ju Sun · 2020
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Machine learning and computational mathematics
E Weinan · 2020
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Robust recovery via implicit bias of discrepant learning rates for double over-parameterization
Chong You, Zhihui Zhu, Qing Qu, and Yi Ma · 2020
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Rethinking deep image prior for denoising
Yeonsik Jo, Se Young Chun, and Jonghyun Choi · 2021
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Untrained neural network priors for inverse imaging problems: A survey
Adnan Qayyum, Inaam Ilahi, Fahad Shamshad, Farid Boussaid, Mohammed Bennamoun, and Junaid Qadir · 2021
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On measuring and controlling the spectral bias of the deep image prior
Zenglin Shi, Pascal Mettes, Subhransu Maji, and Cees G. M. Snoek · 2021
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A plug-and-play deep image prior
Zhaodong Sun, Fabian Latorre, Thomas Sanchez, and Volkan Cevher · 2021
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