Maximal Sparsity with Deep Networks?

5 May 2016

Papers citing "Maximal Sparsity with Deep Networks?"

40 / 40 papers shown

Title
Deep greedy unfolding: Sorting out argsorting in greedy sparse recovery algorithms Sina Mohammad-Taheri Matthew J. Colbrook Simone Brugiapaglia 16 0 0 21 May 2025
WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm Geoffrey Kasenbacher Felix Ehret Gerrit Ecke Sebastian Otte 49 0 0 24 Oct 2024
SINET: Sparsity-driven Interpretable Neural Network for Underwater Image Enhancement Gargi Panda Soumitra Kundu Saumik Bhattacharya Aurobinda Routray 43 0 0 02 Sep 2024
TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds Jingkai Yan Shiyu Wang Xinyu Rain Wei Jimmy Wang Z. Márka S. Márka John N. Wright 29 1 0 16 Oct 2023
Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks Ziyang Zheng Wenrui Dai Duoduo Xue Chenglin Li Junni Zou H. Xiong 44 17 0 25 Apr 2022
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks Ekkehard Schnoor Arash Behboodi Holger Rauhut 24 13 0 08 Dec 2021
Hyperparameter Tuning is All You Need for LISTA Xiaohan Chen Jialin Liu Zhangyang Wang W. Yin ODL 30 23 0 29 Oct 2021
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection HanQin Cai Jialin Liu W. Yin 41 39 0 11 Oct 2021
Deep Algorithm Unrolling for Biomedical Imaging Yuelong Li Or Bar-Shira V. Monga Yonina C. Eldar SyDa 36 10 0 15 Aug 2021
Sparse Bayesian Learning via Stepwise Regression Sebastian Ament Carla P. Gomes 16 5 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 61 225 0 23 Mar 2021
Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth Wei Chen Bowen Zhang Shimei Jin B. Ai Z. Zhong 10 24 0 29 Oct 2020
Convergence Acceleration via Chebyshev Step: Plausible Interpretation of Deep-Unfolded Gradient Descent Satoshi Takabe T. Wadayama 23 10 0 26 Oct 2020
Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction Yunmei Chen Hongcheng Liu X. Ye Qingchao Zhang 61 23 0 22 Jul 2020
When and How Can Deep Generative Models be Inverted? Aviad Aberdam Dror Simon Michael Elad 21 13 0 28 Jun 2020
Safeguarded Learned Convex Optimization Howard Heaton Xiaohan Chen Zhangyang Wang W. Yin 24 22 0 04 Mar 2020
Ada-LISTA: Learned Solvers Adaptive to Varying Models Aviad Aberdam Alona Golts Michael Elad 27 40 0 23 Jan 2020
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing V. Monga Yuelong Li Yonina C. Eldar 46 1,002 0 22 Dec 2019
Multimodal Image Super-resolution via Deep Unfolding with Side Information Iman Marivani Evaggelia Tsiligianni Bruno Cornelis Nikos Deligiannis SupR 32 17 0 18 Oct 2019
Learned-SBL: A Deep Learning Architecture for Sparse Signal Recovery Rubin Jose Peter C. Murthy 18 4 0 17 Sep 2019
Data-driven Estimation of Sinusoid Frequencies Gautier Izacard S. Mohan C. Fernandez‐Granda 16 51 0 03 Jun 2019
Learning step sizes for unfolded sparse coding Pierre Ablin Thomas Moreau Mathurin Massias Alexandre Gramfort MQ 27 51 0 27 May 2019
Tree Search Network for Sparse Regression Kyung-Su Kim Sae-Young Chung 17 1 0 01 Apr 2019
Designing recurrent neural networks by unfolding an l1-l1 minimization algorithm Hung Duy Le Huynh Van Luong Nikos Deligiannis 15 15 0 18 Feb 2019
Regularization by architecture: A deep prior approach for inverse problems Sören Dittmer T. Kluth Peter Maass Daniel Otero Baguer 35 97 0 10 Dec 2018
A Learning-Based Framework for Line-Spectra Super-resolution Gautier Izacard B. Bernstein C. Fernandez‐Granda 18 34 0 14 Nov 2018
Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging Michael R. Kellman E. Bostan N. Repina Laura Waller 17 125 0 10 Aug 2018
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors Danil Kuzin Olga Isupova Lyudmila Mihaylova 28 8 0 15 Jul 2018
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling Shanshan Wu A. Dimakis Sujay Sanghavi Felix X. Yu D. Holtmann-Rice Dmitry Storcheus Afshin Rostamizadeh Sanjiv Kumar SSL 23 53 0 26 Jun 2018
Random mesh projectors for inverse problems Sidharth Gupta K. Kothari Maarten V. de Hoop Ivan Dokmanić 29 15 0 29 May 2018
Neural Inverse Rendering for General Reflectance Photometric Stereo Tatsunori Taniai Takanori Maehara 30 103 0 28 Feb 2018
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding Dong Liu Ke Sun Zhangyang Wang Runsheng Liu Zhengjun Zha 24 12 0 28 Feb 2018
Denoising Prior Driven Deep Neural Network for Image Restoration W. Dong Peiyao Wang W. Yin Guangming Shi Fangfang Wu Xiaotong Lu SupR 39 417 0 21 Jan 2018
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems J. C. Ye Yoseob Han Eunju Cha 36 16 0 03 Jul 2017
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI Yan Yang Jian Sun Huibin Li Zongben Xu MedIm 32 126 0 19 May 2017
Deep Convolutional Neural Network for Inverse Problems in Imaging Kyong Hwan Jin Michael T. McCann Emmanuel Froustey M. Unser 15 2,104 0 11 Nov 2016
Understanding Trainable Sparse Coding via Matrix Factorization Thomas Moreau Joan Bruna 18 44 0 01 Sep 2016
Stacked Approximated Regression Machine: A Simple Deep Learning Approach Zhangyang Wang Shiyu Chang Qing Ling Shuai Huang Xia Hu Honghui Shi Thomas S. Huang BDL 15 2 0 14 Aug 2016
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding Vardan Papyan Yaniv Romano Michael Elad 59 284 0 27 Jul 2016
Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems Raja Giryes Yonina C. Eldar A. Bronstein Guillermo Sapiro 17 85 0 30 May 2016