NETT: Solving Inverse Problems with Deep Neural Networks

28 February 2018

Papers citing "NETT: Solving Inverse Problems with Deep Neural Networks"

50 / 100 papers shown

Title
Good Things Come in Pairs: Paired Autoencoders for Inverse Problems Matthias Chung B. Peters Michael Solomon 31 0 0 10 May 2025
Enhanced uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems Andrea Tonini Luca Dede' UQCV BDL 89 0 0 18 Feb 2025
Deep Guess acceleration for explainable image reconstruction in sparse-view CT E. L. Piccolomini Davide Evangelista E. Morotti OOD 72 1 0 02 Dec 2024
A Primal-dual algorithm for image reconstruction with ICNNs Hok Shing Wong Matthias Joachim Ehrhardt Subhadip Mukherjee 26 1 0 16 Oct 2024
Differentiable programming across the PDE and Machine Learning barrier N. Bouziani David A. Ham Ado Farsi PINN AI4CE 32 1 0 09 Sep 2024
Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models S. Ozaki S. Kaji T. Imae K. Nawa H. Yamashita K. Nakagawa DiffM MedIm 32 0 0 06 Aug 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 22 0 0 15 Jul 2024
Iteratively Refined Image Reconstruction with Learned Attentive Regularizers Mehrsa Pourya Sebastian Neumayer Michael Unser 38 0 0 09 Jul 2024
Blind Inversion using Latent Diffusion Priors Weimin Bai Siyi Chen Wenzheng Chen He Sun DiffM 26 4 0 01 Jul 2024
Stability of Data-Dependent Ridge-Regularization for Inverse Problems Sebastian Neumayer Fabian Altekrüger 34 1 0 18 Jun 2024
FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model Zezheng Song Jiaxin Yuan Haizhao Yang AI4CE 38 16 0 23 Apr 2024
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation Hongwei Tan Ziruo Cai Marcelo Pereyra Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb SSL 67 1 0 08 Apr 2024
Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview Alexander Auras Kanchana Vaishnavi Gandikota Hannah Droege Michael Moeller AAML 29 0 0 19 Feb 2024
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation Zakhar Shumaylov Jeremy Budd Subhadip Mukherjee Carola-Bibiane Schönlieb 21 6 0 01 Feb 2024
Learned reconstruction methods for inverse problems: sample error estimates Luca Ratti 24 0 0 21 Dec 2023
Learned Regularization for Inverse Problems: Insights from a Spectral Model Martin Burger Samira Kabri 25 0 0 15 Dec 2023
What's in a Prior? Learned Proximal Networks for Inverse Problems Zhenghan Fang Sam Buchanan Jeremias Sulam 31 11 0 22 Oct 2023
Provably Convergent Data-Driven Convex-Nonconvex Regularization Zakhar Shumaylov Jeremy Budd Subhadip Mukherjee Carola-Bibiane Schönlieb 30 5 0 09 Oct 2023
Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models Marien Renaud Jiaming Liu Valentin De Bortoli Andrés Almansa Ulugbek S. Kamilov 48 5 0 05 Oct 2023
Solving Low-Dose CT Reconstruction via GAN with Local Coherence Wenjie Liu MedIm 11 2 0 24 Sep 2023
Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems Nathan Buskulic M. Fadili Yvain Quéau 11 4 0 21 Sep 2023
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms Alexis Goujon Sebastian Neumayer M. Unser 41 23 0 21 Aug 2023
InVAErt networks: a data-driven framework for model synthesis and identifiability analysis Guoxiang Grayson Tong Carlos A. Sing Long Daniele E. Schiavazzi 23 7 0 24 Jul 2023
Convergent regularization in inverse problems and linear plug-and-play denoisers A. Hauptmann Subhadip Mukherjee Carola-Bibiane Schönlieb Ferdia Sherry 21 13 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 10 2 0 04 Jul 2023
Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem Valentin Munteanu V. Starostin Alessandro Greco L. Pithan A. Gerlach A. Hinderhofer S. Kowarik F. Schreiber 14 3 0 28 Jun 2023
Globally injective and bijective neural operators Takashi Furuya Michael Puthawala Matti Lassas Maarten V. de Hoop 25 11 0 06 Jun 2023
Convergence analysis of equilibrium methods for inverse problems D. Obmann Markus Haltmeier 21 5 0 02 Jun 2023
A Direct Sampling-Based Deep Learning Approach for Inverse Medium Scattering Problems Jianfeng Ning Fuqun Han Jun Zou 26 11 0 29 Apr 2023
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks B. Afkham Julianne Chung Matthias Chung 24 1 0 17 Apr 2023
Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction Christoph Angermann Simon Göppel Markus Haltmeier 28 2 0 14 Apr 2023
Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography A. Hauptmann Jenni Poimala MedIm 31 5 0 04 Apr 2023
Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs M. S. Feinler B. Hahn MedIm 10 5 0 30 Mar 2023
A Lifted Bregman Formulation for the Inversion of Deep Neural Networks Xiaoyu Wang Martin Benning 28 2 0 01 Mar 2023
Learned Interferometric Imaging for the SPIDER Instrument Matthijs Mars M. Betcke Jason D. McEwen 14 3 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 24 1 0 17 Jan 2023
Convergent Data-driven Regularizations for CT Reconstruction Samira Kabri Alexander Auras D. Riccio Hartmut Bauermeister Martin Benning Michael Moeller Martin Burger 22 12 0 14 Dec 2022
Application of machine learning regression models to inverse eigenvalue problems Nikolaos Pallikarakis Andreas Ntargaras 11 8 0 08 Dec 2022
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems Derick Nganyu Tanyu Jianfeng Ning Tom Freudenberg Nick Heilenkötter A. Rademacher U. Iben Peter Maass AI4CE 18 34 0 06 Dec 2022
Estimation of fibre architecture and scar in myocardial tissue using electrograms: an in-silico study Konstantinos Ntagiantas E. Pignatelli N. Peters C. Cantwell R. Chowdhury Anil A. Bharath 13 1 0 06 Dec 2022
Data-driven Science and Machine Learning Methods in Laser-Plasma Physics Andreas Döpp C. Eberle S. Howard F. Irshad Jinpu Lin M. Streeter AI4CE 24 63 0 30 Nov 2022
Deep unfolding as iterative regularization for imaging inverse problems Zhuoxu Cui Qingyong Zhu Jing Cheng Dong Liang 32 5 0 24 Nov 2022
A Neural-Network-Based Convex Regularizer for Inverse Problems Alexis Goujon Sebastian Neumayer Pakshal Bohra Stanislas Ducotterd M. Unser 11 26 0 22 Nov 2022
Limitations of neural network training due to numerical instability of backpropagation Clemens Karner V. Kazeev P. Petersen 32 3 0 03 Oct 2022
Statistical Learning and Inverse Problems: A Stochastic Gradient Approach Yuri S. Fonseca Yuri F. Saporito 35 4 0 29 Sep 2022
Deep Preconditioners and their application to seismic wavefield processing M. Ravasi 31 2 0 20 Jul 2022
Learned reconstruction methods with convergence guarantees Subhadip Mukherjee A. Hauptmann Ozan Oktem Marcelo Pereyra Carola-Bibiane Schönlieb 22 49 0 11 Jun 2022
Can We Use Neural Regularization to Solve Depth Super-Resolution? Milena Gazdieva Oleg Voynov Alexey Artemov Youyi Zheng Luiz Velho Evgeny Burnaev SupR MDE 14 0 0 21 Dec 2021
Automatic differentiation approach for reconstructing spectral functions with neural networks Lingxiao Wang S. Shi Kai Zhou 14 7 0 12 Dec 2021
Reconstructing spectral functions via automatic differentiation Lingxiao Wang S. Shi Kai Zhou 14 23 0 29 Nov 2021