Adversarial Regularizers in Inverse Problems

29 May 2018

Sebastian Lunz

Ozan Oktem

Carola-Bibiane Schönlieb

GAN

MedIm

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Papers citing "Adversarial Regularizers in Inverse Problems"

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Title
Good Things Come in Pairs: Paired Autoencoders for Inverse Problems Matthias Chung Bas Peters Michael Solomon 34 0 0 10 May 2025
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups Zakhar Shumaylov Peter Zaika James Rowbottom Ferdia Sherry Melanie Weber Carola-Bibiane Schönlieb 41 1 0 03 Oct 2024
Differentiable programming across the PDE and Machine Learning barrier N. Bouziani David A. Ham Ado Farsi PINN AI4CE 37 1 0 09 Sep 2024
Solving the inverse problem of microscopy deconvolution with a residual Beylkin-Coifman-Rokhlin neural network Rui Li M. Kudryashev Artur Yakimovich 32 1 0 03 Jul 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
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction Moritz Piening Fabian Altekrüger J. Hertrich Paul Hagemann Andrea Walther Gabriele Steidl 24 6 0 27 Dec 2023
Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler Jiayu Qian Yuanyuan Liu Jingya Yang Qingping Zhou 23 0 0 24 Oct 2023
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
A Lifted Bregman Formulation for the Inversion of Deep Neural Networks Xiaoyu Wang Martin Benning 33 2 0 01 Mar 2023
Learning Gradients of Convex Functions with Monotone Gradient Networks Shreyas Chaudhari Srinivasa Pranav J. M. F. Moura 17 6 0 25 Jan 2023
Optimal Regularization for a Data Source Oscar Leong Eliza O'Reilly Yong Sheng Soh V. Chandrasekaran 25 4 0 27 Dec 2022
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
A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applications Tristan Milne Étienne Bilocq A. Nachman OT 27 3 0 02 Nov 2022
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs Sebastian Springer Aldo Glielmo A. Senchukova T. Kauppi Jarkko Suuronen L. Roininen H. Haario A. Hauptmann 16 2 0 20 Jun 2022
The Mathematics of Artificial Intelligence Gitta Kutyniok 14 0 0 16 Mar 2022
Generalized Normalizing Flows via Markov Chains Paul Hagemann J. Hertrich Gabriele Steidl BDL DiffM AI4CE 30 22 0 24 Nov 2021
StyleGAN-induced data-driven regularization for inverse problems Arthur Conmy Subhadip Mukherjee Carola-Bibiane Schönlieb GAN 21 3 0 07 Oct 2021
Gradient Step Denoiser for convergent Plug-and-Play Samuel Hurault Arthur Leclaire Nicolas Papadakis 33 93 0 07 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
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance) Jan Stanczuk Christian Etmann L. Kreusser Carola-Bibiane Schönlieb GAN 16 48 0 02 Mar 2021
Convergence and finite sample approximations of entropic regularized Wasserstein distances in Gaussian and RKHS settings M. H. Quang 56 5 0 05 Jan 2021
Model Adaptation for Inverse Problems in Imaging Davis Gilton Greg Ongie Rebecca Willett OOD MedIm 16 48 0 30 Nov 2020
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems Masaki Ikuta Jun Zhang GAN 11 3 0 11 Aug 2020
Consistency analysis of bilevel data-driven learning in inverse problems Neil K. Chada C. Schillings Xin T. Tong Simon Weissmann 17 8 0 06 Jul 2020
Total Deep Variation: A Stable Regularizer for Inverse Problems Erich Kobler Alexander Effland K. Kunisch T. Pock MedIm 19 19 0 15 Jun 2020
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods Daniel Otero Baguer Johannes Leuschner Maximilian Schmidt 29 186 0 10 Mar 2020
Deep synthesis regularization of inverse problems D. Obmann Johannes Schwab Markus Haltmeier 15 11 0 01 Feb 2020
Bayesian Inference with Generative Adversarial Network Priors Dhruv V. Patel Assad A. Oberai GAN AI4CE 25 17 0 22 Jul 2019
A Projectional Ansatz to Reconstruction Sören Dittmer Peter Maass 14 3 0 10 Jul 2019
The Oracle of DLphi Dominik Alfke W. Baines J. Blechschmidt Mauricio J. del Razo Sarmina Amnon Drory ... L. Thesing Philipp Trunschke Johannes von Lindheim David Weber Melanie Weber 37 0 0 17 Jan 2019
Random mesh projectors for inverse problems Sidharth Gupta K. Kothari Maarten V. de Hoop Ivan Dokmanić 29 15 0 29 May 2018
NETT: Solving Inverse Problems with Deep Neural Networks Housen Li Johannes Schwab Stephan Antholzer Markus Haltmeier 31 238 0 28 Feb 2018