Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
All Papers
0 / 0 papers shown
Home
Papers
1803.00092
Cited By
v1
v2
v3 (latest)
NETT: Solving Inverse Problems with Deep Neural Networks
Inverse Problems (IP), 2018
28 February 2018
Housen Li
Johannes Schwab
Stephan Antholzer
Markus Haltmeier
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"NETT: Solving Inverse Problems with Deep Neural Networks"
50 / 87 papers shown
Why the noise model matters: A performance gap in learned regularization
Sebastian Banert
Christoph Brauer
D. Lorenz
Lionel Tondji
89
1
0
14 Oct 2025
Solving Inverse Problems in Stochastic Self-Organizing Systems through Invariant Representations
Elias Najarro
Nicolas Bessone
S. Risi
CML
234
0
0
13 Jun 2025
Deep regularization networks for inverse problems with noisy operators
Fatemeh Pourahmadian
Yang Xu
186
0
0
08 Jun 2025
Good Things Come in Pairs: Paired Autoencoders for Inverse Problems
Matthias Chung
Bas Peters
Michael Solomon
255
4
0
10 May 2025
Enhanced uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems
Andrea Tonini
Luca Dede'
UQCV
BDL
264
0
0
18 Feb 2025
Stability Bounds for the Unfolded Forward-Backward Algorithm
Journal of Mathematical Imaging and Vision (JMIV), 2024
Émilie Chouzenoux
Cecile Della Valle
J. Pesquet
247
1
0
23 Dec 2024
A primal-dual algorithm for image reconstruction with input-convex neural network regularizers
Matthias Joachim Ehrhardt
Matthias Joachim Ehrhardt
Subhadip Mukherjee
237
1
0
16 Oct 2024
Differentiable programming across the PDE and Machine Learning barrier
N. Bouziani
David A. Ham
Ado Farsi
PINN
AI4CE
270
4
0
09 Sep 2024
LIP-CAR: contrast agent reduction by a deep learned inverse problem
Davide Bianchi
Sonia Colombo Serra
Davide Evangelista
Pengpeng Luo
E. Morotti
Giovanni Valbusa
MedIm
161
0
0
15 Jul 2024
Stability of Data-Dependent Ridge-Regularization for Inverse Problems
Sebastian Neumayer
Fabian Altekrüger
355
2
0
18 Jun 2024
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation
Hongwei Tan
Ziruo Cai
Marcelo Pereyra
Subhadip Mukherjee
Junqi Tang
Carola-Bibiane Schönlieb
SSL
354
3
0
08 Apr 2024
Learned reconstruction methods for inverse problems: sample error estimates
Luca Ratti
225
0
0
21 Dec 2023
Learned Regularization for Inverse Problems: Insights from a Spectral Model
Martin Burger
Samira Kabri
262
1
0
15 Dec 2023
What's in a Prior? Learned Proximal Networks for Inverse Problems
International Conference on Learning Representations (ICLR), 2023
Zhenghan Fang
Sam Buchanan
Jeremias Sulam
344
23
0
22 Oct 2023
Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models
International Conference on Learning Representations (ICLR), 2023
Marien Renaud
Jiaming Liu
Valentin De Bortoli
Andrés Almansa
Ulugbek S. Kamilov
410
6
0
05 Oct 2023
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms
SIAM Journal of Imaging Sciences (JSIS), 2023
Alexis Goujon
Sebastian Neumayer
M. Unser
232
40
0
21 Aug 2023
Convergent regularization in inverse problems and linear plug-and-play denoisers
Foundations of Computational Mathematics (FoCM), 2023
A. Hauptmann
Subhadip Mukherjee
Carola-Bibiane Schönlieb
Ferdia Sherry
219
18
0
18 Jul 2023
Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion
Nishant Kumar
L. Krause
T. Wondrak
S. Eckert
Kerstin Eckert
Stefan Gumhold
132
2
0
04 Jul 2023
Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem
Journal of Applied Crystallography (J. Appl. Cryst.), 2023
Valentin Munteanu
V. Starostin
Alessandro Greco
L. Pithan
A. Gerlach
A. Hinderhofer
S. Kowarik
F. Schreiber
178
5
0
28 Jun 2023
Globally injective and bijective neural operators
Neural Information Processing Systems (NeurIPS), 2023
Takashi Furuya
Michael Puthawala
Matti Lassas
Maarten V. de Hoop
231
15
0
06 Jun 2023
Convergence analysis of equilibrium methods for inverse problems
D. Obmann
Gyeongha Hwang
Markus Haltmeier
153
5
0
02 Jun 2023
A Direct Sampling-Based Deep Learning Approach for Inverse Medium Scattering Problems
Inverse Problems (IP), 2023
Jianfeng Ning
Fuqun Han
Jun Zou
234
18
0
29 Apr 2023
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks
B. Afkham
Julianne Chung
Matthias Chung
221
1
0
17 Apr 2023
Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography
A. Hauptmann
Jenni Poimala
MedIm
159
7
0
04 Apr 2023
Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs
M. S. Feinler
B. Hahn
MedIm
113
7
0
30 Mar 2023
A Lifted Bregman Formulation for the Inversion of Deep Neural Networks
Frontiers in Applied Mathematics and Statistics (FAMS), 2023
Xiaoyu Wang
Martin Benning
173
3
0
01 Mar 2023
Learned Interferometric Imaging for the SPIDER Instrument
RAS Techniques and Instruments (RTI), 2023
Matthijs Mars
M. Betcke
Jason D. McEwen
130
4
0
24 Jan 2023
Cross-domain Self-supervised Framework for Photoacoustic Computed Tomography Image Reconstruction
Hengrong Lan
Lijie Huang
Zhiqiang Li
Jing Lv
Jianwen Luo
ViT
OOD
163
1
0
17 Jan 2023
Convergent Data-driven Regularizations for CT Reconstruction
Communication on Applied Mathematics and Computation (CAMC), 2022
Samira Kabri
Alexander Auras
D. Riccio
Hartmut Bauermeister
Martin Benning
Michael Moeller
Martin Burger
204
13
0
14 Dec 2022
Application of machine learning regression models to inverse eigenvalue problems
Computers and Mathematics with Applications (CMA), 2022
Nikolaos Pallikarakis
Andreas Ntargaras
103
14
0
08 Dec 2022
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems
Inverse Problems (IP), 2022
Derick Nganyu Tanyu
Jianfeng Ning
Tom Freudenberg
Nick Heilenkötter
A. Rademacher
U. Iben
Peter Maass
AI4CE
264
52
0
06 Dec 2022
Data-driven Science and Machine Learning Methods in Laser-Plasma Physics
High Power Laser Science and Engineering (HPLSE), 2022
Andreas Döpp
C. Eberle
S. Howard
F. Irshad
Jinpu Lin
M. Streeter
AI4CE
301
88
0
30 Nov 2022
Deep unfolding as iterative regularization for imaging inverse problems
Inverse Problems (IP), 2022
Zhuoxu Cui
Qingyong Zhu
Jing Cheng
Dong Liang
178
11
0
24 Nov 2022
A Neural-Network-Based Convex Regularizer for Inverse Problems
IEEE Transactions on Computational Imaging (TCI), 2022
Alexis Goujon
Sebastian Neumayer
Pakshal Bohra
Stanislas Ducotterd
M. Unser
370
39
0
22 Nov 2022
Statistical Learning and Inverse Problems: A Stochastic Gradient Approach
Neural Information Processing Systems (NeurIPS), 2022
Yuri S. Fonseca
Yuri F. Saporito
222
6
0
29 Sep 2022
Deep Preconditioners and their application to seismic wavefield processing
Frontiers in Earth Science (FES), 2022
M. Ravasi
227
3
0
20 Jul 2022
Learned reconstruction methods with convergence guarantees
IEEE Signal Processing Magazine (IEEE Signal Process. Mag.), 2022
Subhadip Mukherjee
A. Hauptmann
Ozan Oktem
Marcelo Pereyra
Carola-Bibiane Schönlieb
405
65
0
11 Jun 2022
Can We Use Neural Regularization to Solve Depth Super-Resolution?
VISIGRAPP (VISIGRAPP), 2021
Milena Gazdieva
Oleg Voynov
Alexey Artemov
Youyi Zheng
Luiz Velho
Evgeny Burnaev
SupR
MDE
120
0
0
21 Dec 2021
Automatic differentiation approach for reconstructing spectral functions with neural networks
Lingxiao Wang
S. Shi
Kai Zhou
100
7
0
12 Dec 2021
Reconstructing spectral functions via automatic differentiation
Lingxiao Wang
S. Shi
Kai Zhou
131
27
0
29 Nov 2021
Deep Learning Adapted Acceleration for Limited-view Photoacoustic Computed Tomography
Hengrong Lan
Jiali Gong
Fei Gao
88
8
0
08 Nov 2021
Survey of Deep Learning Methods for Inverse Problems
S. Kamyab
Zihreh Azimifar
Rasool Sabzi
Paul Fieguth
212
5
0
07 Nov 2021
Learning convex regularizers satisfying the variational source condition for inverse problems
Subhadip Mukherjee
Antonio Bonafonte
Mateusz Lajszczak
133
12
0
24 Oct 2021
StyleGAN-induced data-driven regularization for inverse problems
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021
Arthur Conmy
Subhadip Mukherjee
Carola-Bibiane Schönlieb
GAN
130
4
0
07 Oct 2021
Deep learning based dictionary learning and tomographic image reconstruction
SIAM Journal of Imaging Sciences (SIAM J. Imaging Sci.), 2021
Jevgenija Rudzusika
Thomas Koehler
Ozan Oktem
203
3
0
26 Aug 2021
Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future
Progress in Biomedical Engineering (PBE), 2021
Andreas K. Maier
Harald Kostler
M. Heisig
P. Krauss
S. Yang
MedIm
212
35
0
10 Aug 2021
Regularising Inverse Problems with Generative Machine Learning Models
Journal of Mathematical Imaging and Vision (JMIV), 2021
Margaret Duff
Neill D. F. Campbell
Matthias Joachim Ehrhardt
GAN
MedIm
225
43
0
22 Jul 2021
Inverse Problem of Nonlinear Schrödinger Equation as Learning of Convolutional Neural Network
Yiran Wang
Zhen Li
99
3
0
19 Jul 2021
Learning the optimal Tikhonov regularizer for inverse problems
Neural Information Processing Systems (NeurIPS), 2021
Giovanni S. Alberti
Ernesto De Vito
Matti Lassas
Luca Ratti
Matteo Santacesaria
192
40
0
11 Jun 2021
End-to-end reconstruction meets data-driven regularization for inverse problems
Neural Information Processing Systems (NeurIPS), 2021
Subhadip Mukherjee
M. Carioni
Ozan Oktem
Carola-Bibiane Schönlieb
164
46
0
07 Jun 2021
1
2
Next
