{"total":53,"rows":[{"id":"2410.02423","title":"PnP-Flow: Plug-and-Play Image Restoration with Flow Matching","created":"2024-10-03","cited_by_count":97,"area":"diffusion"},{"id":"2504.10612","title":"Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling","created":"2025-04-14","cited_by_count":27,"area":"diffusion"},{"id":"2509.26287","title":"Flower: A Flow-Matching Solver for Inverse Problems","created":"2025-09-30","cited_by_count":27,"area":"diffusion"},{"id":"2506.03979","title":"Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach","created":"2025-06-04","cited_by_count":18,"area":"diffusion"},{"id":"2501.16839","title":"Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans","created":"2025-01-28","cited_by_count":17,"area":"diffusion"},{"id":"2509.13399","title":"EdiVal-Agent: An Object-Centric Framework for Automated, Fine-Grained Evaluation of Multi-Turn Editing","created":"2025-09-16","cited_by_count":10,"area":"agents"},{"id":"2503.07578","title":"Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation","created":"2025-03-10","cited_by_count":7,"area":"diffusion"},{"id":"2510.24830","title":"The Generation Phases of Flow Matching: a Denoising Perspective","created":"2025-10-28","cited_by_count":7,"area":"diffusion"},{"id":"2505.10311","title":"Whitened Score Diffusion: A Structured Prior for Imaging Inverse Problems","created":"2025-05-15","cited_by_count":6,"area":"diffusion"},{"id":"2505.06549","title":"Good Things Come in Pairs: Paired Autoencoders for Inverse Problems","created":"2025-05-10","cited_by_count":5,"area":null},{"id":"2506.14605","title":"Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching","created":"2025-06-17","cited_by_count":5,"area":null},{"id":"2507.15397","title":"MAP Estimation with Denoisers: Convergence Rates and Guarantees","created":"2025-07-21","cited_by_count":5,"area":null},{"id":"2411.17472","title":"Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory","created":"2024-11-25","cited_by_count":3,"area":"diffusion"},{"id":"2508.04605","title":"Multitask Learning with Stochastic Interpolants","created":"2025-08-06","cited_by_count":3,"area":"diffusion"},{"id":"2509.20511","title":"A Recovery Theory for Diffusion Priors: Deterministic Analysis of the Implicit Prior Algorithm","created":"2025-09-24","cited_by_count":3,"area":"diffusion"},{"id":"2512.04283","title":"Plug-and-Play Image Restoration with Flow Matching: A Continuous Viewpoint","created":"2025-12-03","cited_by_count":3,"area":"diffusion"},{"id":"2506.05240","title":"Aligning Latent Spaces with Flow Priors","created":"2025-06-05","cited_by_count":2,"area":null},{"id":"2510.04823","title":"Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis","created":"2025-10-06","cited_by_count":2,"area":"diffusion"},{"id":"2511.06138","title":"Latent Refinement via Flow Matching for Training-free Linear Inverse Problem Solving","created":"2025-11-08","cited_by_count":2,"area":"diffusion"},{"id":"2511.06748","title":"Image Restoration via Primal Dual Hybrid Gradient and Flow Generative Model","created":"2025-11-10","cited_by_count":2,"area":null},{"id":"2512.16768","title":"On The Hidden Biases of Flow Matching Samplers","created":"2025-12-18","cited_by_count":2,"area":"diffusion"},{"id":"2602.08538","title":"Trajectory Stitching for Solving Inverse Problems with Flow-Based Models","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2603.13659","title":"FMS$^2$: Unified Flow Matching for Segmentation and Synthesis of Thin Structures","created":"2026-03-14","cited_by_count":2,"area":"diffusion"},{"id":"2603.14135","title":"Conditional flow matching for physics-constrained inverse problems with finite training data","created":"2026-03-14","cited_by_count":2,"area":"diffusion"},{"id":"2605.03749","title":"FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution","created":"2026-05-05","cited_by_count":2,"area":null},{"id":"2606.22696","title":"NullFlow: One-Step Generative Reconstruction","created":"2026-06-21","cited_by_count":2,"area":null},{"id":"2506.15929","title":"Moir\\'eXNet: Adaptive Multi-Scale Demoir\\'eing with Linear Attention Test-Time Training and Truncated Flow Matching Prior","created":"2025-06-19","cited_by_count":1,"area":"diffusion"},{"id":"2511.20152","title":"Restora-Flow: Mask-Guided Image Restoration with Flow Matching","created":"2025-11-25","cited_by_count":1,"area":"diffusion"},{"id":"2512.08125","title":"FlowSteer: Conditioning Flow Field for Consistent Image Restoration","created":"2025-12-09","cited_by_count":1,"area":null},{"id":"2512.17493","title":"UPMRI: Unsupervised Parallel MRI Reconstruction via Projected Conditional Flow Matching","created":"2025-12-19","cited_by_count":1,"area":"diffusion"},{"id":"2601.23231","title":"Solving Inverse Problems with Flow-based Models via Model Predictive Control","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2603.07357","title":"Tunable Latent Generative Priors for Compressed Sensing and Inverse Problems","created":"2026-03-07","cited_by_count":1,"area":null},{"id":"2603.14798","title":"Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces","created":"2026-03-16","cited_by_count":1,"area":null},{"id":"2606.01985","title":"MT-EditFlow: Reinforcement Learning for Multi-Turn Image Editing with Flow Matching","created":"2026-06-01","cited_by_count":1,"area":"diffusion"},{"id":"2606.16396","title":"SP$^3$: Spherical Priors for Plug-and-Play Restoration","created":"2026-06-15","cited_by_count":1,"area":null},{"id":"2606.19019","title":"FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelity","created":"2026-06-17","cited_by_count":1,"area":"diffusion"},{"id":"2608.07199","title":"Flow-Corrected Shape Optimization: Taming Manifold Drift in High-Dimensional 3D Models","created":"2026-08-07","cited_by_count":1,"area":"diffusion"},{"id":"2601.08527","title":"Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs","created":"2026-01-13","cited_by_count":0,"area":null},{"id":"2603.00205","title":"Efficient Flow Matching for Sparse-View CT Reconstruction","created":"2026-02-27","cited_by_count":0,"area":"diffusion"},{"id":"2604.09227","title":"Training-free, Perceptually Consistent Low-Resolution Previews with High-Resolution Image for Efficient Workflows of Diffusion Models","created":"2026-04-10","cited_by_count":0,"area":"diffusion"},{"id":"2605.08328","title":"P-Flow: Proxy-gradient Flows for Linear Inverse Problems","created":"2026-05-08","cited_by_count":0,"area":"diffusion"},{"id":"2605.08640","title":"FlowADMM: Plug-and-play ADMM with Flow-based Renoise-Denoise Priors","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2607.14272","title":"Lyapunov Guidance: A Unified Framework for Stabilizing Generative Flows","created":"2026-07-15","cited_by_count":0,"area":"diffusion"},{"id":"2607.23946","title":"Joint Flow Matching for Generator-Consistent Classification","created":"2026-07-27","cited_by_count":0,"area":"diffusion"},{"id":"2609.00863","title":"Conditional Flow Matching for ML-Based Inverse Design Problems","created":"2026-09-01","cited_by_count":0,"area":"diffusion"},{"id":"2609.12953","title":"Fast and Faithful: Principled Conditional Flow Matching for Inverse Problems","created":"2026-09-11","cited_by_count":0,"area":"diffusion"},{"id":"2609.20769","title":"FlowSGS: Improving Flow Matching Priors for Inverse Imaging with Stochastic Interpolants","created":"2026-09-17","cited_by_count":0,"area":"diffusion"},{"id":"2609.27486","title":"Active Learning for Low-Altitude Radio Map Construction via Plug-and-Play Flow Matching","created":"2026-09-23","cited_by_count":0,"area":"diffusion"},{"id":"2609.33834","title":"CLIMB-flow: Coupled Linear Inverse posterior sampling via Multiscale-Based flow","created":"2026-09-27","cited_by_count":0,"area":"diffusion"},{"id":"2609.37529","title":"Principled MAP estimation for inverse problems: bridging the gap between convergence and performance","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.03503","title":"Getting Your Guidance Weights Right in diffusion and flow-matching posterior sampling","created":"2026-10-02","cited_by_count":0,"area":"diffusion"},{"id":"2610.06135","title":"AnchorGen: Anchored Optimization for Customizable Generative 3D Design","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.13095","title":"Stochastic Finite-Transition Learning and Exploration for Accelerated MRI Reconstruction","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}