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Generative neural networks have been empirically found very promising in providing effective structural priors for compressed sensing, since they can be trained to span low-dimensional data manifolds in high-dimensional signal spaces.
Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J Candes, Justin K Romberg, and Terence Tao · 2006
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
A survey of compressed sensing
Holger Boche, Robert Calderbank, Gitta Kutyniok, and Jan Vybíral · 2015
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
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.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Earlier work this paper cites.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
Phase retrieval under a generative prior
Paul Hand, Oscar Leong, and Vlad Voroninski · 2018
Cited alongside, same era.
Deep denoising: Rate-optimal recovery of structured signals with a deep prior
Reinhard Heckel, Wen Huang, Paul Hand, and Vladislav Voroninski · 2018
Cited alongside, same era.
A provably convergent scheme for compressive sensing under random generative priors
Wen Huang, Paul Hand, Reinhard Heckel, and Vladislav Voroninski · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
Later among the works it cites.
Compressed sensing with deep image prior and learned regularization
Dave Van Veen, Ajil Jalal, Mahdi Soltanolkotabi, Eric Price, Sriram Vishwanath, and Alexandros G Dimakis · 2018
Later among the works it cites.
Global guarantees for enforcing deep generative priors by empirical risk
Paul Hand and Vladislav Voroninski · 2019
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Inverting deep generative models, one layer at a time
Qi Lei, Ajil Jalal, Inderjit S Dhillon, and Alexandros G Dimakis · 2019
Later among the works it cites.
Shuang Qiu, Xiaohan Wei, and Zhuoran Yang · 2019
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Invertibility of convolutional generative networks from partial measurements
Fangchang Ma, Ulas Ayaz, and Sertac Karaman · 2018
Cited alongside, same era.
A geometric analysis of phase retrieval
Ju Sun, Qing Qu, and John Wright · 2018
Cited alongside, same era.
Jorio Cocola, Paul Hand, and Vladislav Voroninski · 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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