TV Regularization

TV: Total Variation

Latest papers

10 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

TV-Regulated OPD: Direction Matters in On-Policy Distillation
Sep 8, 2026Han Xiao, Yifan Niu, Dongyi Liu +2Preference Optimization On-Policy Distillation
The Hong Kong University of Science and Technology (Guangzhou) · The University of Edinburgh · The Hong Kong University of Science and Technology
A Tensor Variational Formulation of Gradient Energy Total Variation
Aug 29, 2026Freddie Åström, George Baravdish, Michael FelsbergTV Regularization Image Denoising
Computer Vision Laboratory, Linköping University, Sweden · Center for Medical Image Science and Visualization (CMIV), Linköping University · Department of Science and Technology, Linköping University, Sweden
Learning spatially varying regularisation parameters of low regularity for image reconstruction
Aug 25, 2026Kostas Papafitsoros, Luca Calatroni, Andreas KoflerTV Regularization Image Reconstruction
School of Mathematical Sciences, Queen Mary University of London, London E1 4BS, United Kingdom · MaLGa centre, DIBRIS, Universit`a di Genova, Istituto Italiano di Tecnologia, Genoa, Italy · Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
Dual Modality Prompted Diffusion Priors for Zero Shot Hyperspectral Pansharpening
Aug 12, 2026Pengwei Xie, Fei Zhu, Jiajun Li +4Diffusion Models for Image Restoration Hyperspectral Image Super-Resolution
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China · School of GeoAI and Hinton STAI Institute, and the Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China · Peking University, Beijing 100871, China +3
Framelet-Based Blind Image Restoration with Minimax Concave Regularization
Apr 21, 2026Heng Zhang, Reza Parvaz, Rui YangImage Deblurring Image Restoration
College of Artificial Intelligence and Big Data, Chongqing Polytechnic University of Electronic Technology, Chongqing,China · Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Ardabil, Iran
Duality for the Adversarial Total Variation
Apr 20, 2026Leon Bungert, Lucas SchmittTV Regularization
Institute of Mathematics, Center for Artificial Intelligence and Data Science (CAIDAS), University of Würzburg, Emil-Fischer-Str. 40, 97074 Würzburg, Germany
Discrete Flow Matching Policy Optimization
Apr 7, 2026Maojiang Su, Po-Chung Hsieh, Weimin Wu +4TV Regularization RL for Flow Matching Models
Center for Foundation Models and Generative AI, Northwestern University, Evanston, IL 60208, USA · Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan · Department of Physics, National Taiwan University, Taipei 10617, Taiwan +1
A Dual Certificate Approach to Sparsity in Infinite-Width Shallow Neural Networks
Mar 18, 2026Leonardo Del Grande, Christoph Brune, Marcello CarioniTV Regularization Shallow Neural Networks
Department of Applied Mathematics, University of Twente, 7500AE Enschede, The Netherlands
Bayesian Uncertainty-Aware MRI Reconstruction
Mar 13, 2026Ahmed Karam Eldaly, Matteo Figini, Daniel C. AlexanderTV Regularization Accelerated MRI Reconstruction
Department of Computer Science, University of Exeter, UK · UCL Hawkes Institute, Department of Computer Science, University College London, UK
An Improved Variational Method for Image Denoising
Oct 3, 2024Jing-En Huang, Jia-Wei Liao, Ku-Te Lin +2TV Regularization Image Denoising
National Yang Ming Chiao Tung University · National Taiwan University · National Central University +1

Sep 8, 2026J/K move · Enter open · S save

TV-Regulated OPD: Direction Matters in On-Policy Distillation

Han Xiao, Yifan Niu, Dongyi Liu, Chang Luo+1

The Hong Kong University of Science and Technology (Guangzhou) · The University of Edinburgh · The Hong Kong University of Science and Technology

Preference Optimization On-Policy Distillation RL Post-Training TV Regularization

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On-Policy Distillation (OPD) facilitates the transfer of knowledge from domain expert to student in the post-training phase of Large Language Models (LLMs). However, the supervision signals in mainstream OPD methods suffer from high variance and noise which is generally instable during training. In this work, we systematically investigated what really matters to the performance and the fundamental mechanisms behind the instability during training. We found that retaining only the sign of token-level advantages is sufficient to achieve the performance comparable to standard OPD. Meanwhile, smoother and bounded advantages can stabilize the training process without sacrificing its performance. These motivated us to shape the advantages using the Total Variation (TV) and propose a robust TV regulated On-Policy Distillation (TV-OPD) method. Benefiting from the bounded and diminished advantages, TV-OPD exhibits stable training dynamics and steady late-stage performance. We conducted comprehensive experiments and found that, across various settings, TV-OPD consistently achieved better performance and lower variance in the late-stage of training.