Learned convex regularizers for inverse problems

6 August 2020

Carola-Bibiane Schönlieb

ArXiv PDF HTML

Papers citing "Learned convex regularizers for inverse problems"

20 / 20 papers shown

Title
Good Things Come in Pairs: Paired Autoencoders for Inverse Problems Matthias Chung Bas Peters Michael Solomon 34 0 0 10 May 2025
Gradient Networks Shreyas Chaudhari Srinivasa Pranav J. M. F. Moura 58 0 0 28 Jan 2025
A study of why we need to reassess full reference image quality assessment with medical images Anna Breger A. Biguri Malena Sabaté Landman Ian Selby Nicole Amberg ... Lipeng Ning Sören Dittmer Michael Roberts AIX-COVNET Collaboration Carola-Bibiane Schönlieb 39 5 0 29 May 2024
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation Hongwei Tan Ziruo Cai Marcelo Pereyra Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb SSL 70 1 0 08 Apr 2024
What's in a Prior? Learned Proximal Networks for Inverse Problems Zhenghan Fang Sam Buchanan Jeremias Sulam 31 11 0 22 Oct 2023
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms Alexis Goujon Sebastian Neumayer M. Unser 41 23 0 21 Aug 2023
An adaptively inexact first-order method for bilevel optimization with application to hyperparameter learning Mohammad Salehi Subhadip Mukherjee Lindon Roberts Matthias Joachim Ehrhardt 26 5 0 19 Aug 2023
A Lifted Bregman Formulation for the Inversion of Deep Neural Networks Xiaoyu Wang Martin Benning 33 2 0 01 Mar 2023
Analyzing Inexact Hypergradients for Bilevel Learning Matthias Joachim Ehrhardt Lindon Roberts 24 8 0 11 Jan 2023
A Neural-Network-Based Convex Regularizer for Inverse Problems Alexis Goujon Sebastian Neumayer Pakshal Bohra Stanislas Ducotterd M. Unser 16 26 0 22 Nov 2022
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
The Geometry of Adversarial Training in Binary Classification Leon Bungert Nicolas García Trillos Ryan W. Murray AAML 24 22 0 26 Nov 2021
Input Convex Gradient Networks Jack Richter-Powell Jonathan Lorraine Brandon Amos 12 15 0 23 Nov 2021
Stochastic Primal-Dual Deep Unrolling Junqi Tang Subhadip Mukherjee Carola-Bibiane Schönlieb 24 4 0 19 Oct 2021
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods Tobias Alt Karl Schrader Joachim Weickert Pascal Peter M. Augustin 22 4 0 31 Aug 2021
Learning the optimal Tikhonov regularizer for inverse problems Giovanni S. Alberti E. De Vito Matti Lassas Luca Ratti Matteo Santacesaria 25 30 0 11 Jun 2021
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 40 225 0 23 Mar 2021
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie R. Laumont Valentin De Bortoli Andrés Almansa J. Delon Alain Durmus Marcelo Pereyra 24 109 0 08 Mar 2021
Regularization by Denoising: Clarifications and New Interpretations E. T. Reehorst Philip Schniter 56 214 0 06 Jun 2018
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 187 599 0 22 Sep 2016