Deep Unfolding

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Overview

Deep unfolding studies how to convert iterative algorithms—such as proximal-gradient methods, ADMM, and conjugate-gradient solvers—into finite neural networks whose stages correspond to algorithmic updates. It matters when problems have known physical or mathematical structure, including image reconstruction, signal recovery, sensing, and large-scale optimization, where purely learned models may be data-hungry or hard to interpret. Common approaches learn update parameters and priors while preserving constraints or measurement models, sometimes adding adaptive steps, generative priors, or specialized modules for local and nonlocal features. A central challenge is balancing interpretability, speed, and robustness to new conditions against the flexibility and scalability of less structured learned models.

Latest papers

CardsList

37 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

  1. Auto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization

    Mar 18, 2026Ahmet KaplanDeep UnfoldingWireless Communications

  2. Oscillating Dispersion for Maximal Light-throughput Spectral Imaging

    Mar 16, 2026Jiuyun Zhang, Zhan Shi, Linsen Chen +1Computational ImagingHyperspectral Imaging

    Nanjing University, Nanjing, China · Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University, Nanjing, China

  3. Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling

    Feb 24, 2026Jonathan Spence, Tobías I. Liaudat, Konstantinos Zygalakis +1Posterior SamplingGenerative Adversarial Networks

    Maxwell Institute for Mathematical Sciences, Bayes Centre, 47 Potterrow, Edinburgh, Scotland, UK · School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, Scotland, UK · IRFU, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France +1

  4. Deep LoRA-Unfolding Networks for Image Restoration

    Feb 21, 2026Xiangming Wang, Haijin Zeng, Benteng Sun +4Deep UnfoldingImage Restoration

    School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong, China · Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China · School of Intelligence Science and Technology, Nanjing University, Suzhou, China +1

  5. Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction

    Oct 2, 2025Yi Ai, Yuanhao Cai, Yulun Zhang +2Flow MatchingImage Reconstruction

    School of Computer Science, Shanghai Jiao Tong University, China · Department of Computer Science, Johns Hopkins University, USA · School of Engineering, Westlake University, China

  6. SlowFast-SCI: Slow-Fast Deep Unfolding Learning for Spectral Compressive Imaging

    Sep 20, 2025Haijin Zeng, Xuan Lu, Jiezhang Cao +8Image ReconstructionComputational Imaging

    Harbin Institute of Technology, Shenzhen · The Chinese University of Hong Kong, Shenzhen · Shanghai Jiaotong University +3

  7. Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding

    Sep 10, 2025Tam Thuc Do, Philip A. Chou, Gene CheungPoint Cloud CompressionRate-Distortion Optimization

    department of EECS, York University, 4700 Keele Street, Toronto, M3J 1P3, Canada