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Convolutional Proximal Neural Networks and Plug-and-Play Algorithms

Convolutional Proximal Neural Networks and Plug-and-Play Algorithms

4 November 2020
J. Hertrich
Sebastian Neumayer
Gabriele Steidl
ArXivPDFHTML

Papers citing "Convolutional Proximal Neural Networks and Plug-and-Play Algorithms"

17 / 17 papers shown
Title
Approximation theory for 1-Lipschitz ResNets
Approximation theory for 1-Lipschitz ResNets
Davide Murari
Takashi Furuya
Carola-Bibiane Schönlieb
4
0
0
17 May 2025
Reconstruct Anything Model: a lightweight foundation model for computational imaging
Reconstruct Anything Model: a lightweight foundation model for computational imaging
M. Terris
Samuel Hurault
Maxime Song
Julian Tachella
MedIm
DiffM
75
2
0
11 Mar 2025
PnP-Flow: Plug-and-Play Image Restoration with Flow Matching
PnP-Flow: Plug-and-Play Image Restoration with Flow Matching
Ségolène Martin
Anne Gagneux
Paul Hagemann
Gabriele Steidl
42
9
0
03 Oct 2024
Parseval Convolution Operators and Neural Networks
Parseval Convolution Operators and Neural Networks
Michael Unser
Stanislas Ducotterd
25
3
0
19 Aug 2024
Stability of Data-Dependent Ridge-Regularization for Inverse Problems
Stability of Data-Dependent Ridge-Regularization for Inverse Problems
Sebastian Neumayer
Fabian Altekrüger
42
1
0
18 Jun 2024
How Much Training Data is Memorized in Overparameterized Autoencoders?
  An Inverse Problem Perspective on Memorization Evaluation
How Much Training Data is Memorized in Overparameterized Autoencoders? An Inverse Problem Perspective on Memorization Evaluation
Koren Abitbul
Yehuda Dar
TDI
28
2
0
04 Oct 2023
Proximal Residual Flows for Bayesian Inverse Problems
Proximal Residual Flows for Bayesian Inverse Problems
J. Hertrich
BDL
TPM
36
4
0
30 Nov 2022
A Neural-Network-Based Convex Regularizer for Inverse Problems
A Neural-Network-Based Convex Regularizer for Inverse Problems
Alexis Goujon
Sebastian Neumayer
Pakshal Bohra
Stanislas Ducotterd
M. Unser
19
26
0
22 Nov 2022
Improving Lipschitz-Constrained Neural Networks by Learning Activation
  Functions
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Stanislas Ducotterd
Alexis Goujon
Pakshal Bohra
Dimitris Perdios
Sebastian Neumayer
M. Unser
35
12
0
28 Oct 2022
Dynamical systems' based neural networks
Dynamical systems' based neural networks
E. Celledoni
Davide Murari
B. Owren
Carola-Bibiane Schönlieb
Ferdia Sherry
OOD
43
10
0
05 Oct 2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Sebastian Neumayer
Alexis Goujon
Pakshal Bohra
M. Unser
37
16
0
13 Apr 2022
Bayesian Inversion for Nonlinear Imaging Models using Deep Generative
  Priors
Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors
Pakshal Bohra
Thanh-an Michel Pham
Jonathan Dong
M. Unser
MedIm
21
11
0
18 Mar 2022
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for
  Superresolution
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution
Fabian Altekrüger
J. Hertrich
27
15
0
20 Jan 2022
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded
  as Neural Networks
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks
Ekkehard Schnoor
Arash Behboodi
Holger Rauhut
18
13
0
08 Dec 2021
Generalized Normalizing Flows via Markov Chains
Generalized Normalizing Flows via Markov Chains
Paul Hagemann
J. Hertrich
Gabriele Steidl
BDL
DiffM
AI4CE
30
22
0
24 Nov 2021
Gradient Step Denoiser for convergent Plug-and-Play
Gradient Step Denoiser for convergent Plug-and-Play
Samuel Hurault
Arthur Leclaire
Nicolas Papadakis
33
93
0
07 Oct 2021
Wasserstein Patch Prior for Image Superresolution
Wasserstein Patch Prior for Image Superresolution
J. Hertrich
Antoine Houdard
C. Redenbach
SupR
MDE
29
22
0
27 Sep 2021
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