mlsanityData to 14 Oct 2026

Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling

arXiv 2502.16445 ↗First posted 23 Feb 2025cs.LG cs.AI cs.CVcited by 4

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for entertainment to solving inverse problems. Nonetheless, training a generator is a non-trivial feat that requires fine-tuning and can lead to so-called hallucinations, that is, the generation of images that are unrealistic. In this work, we explore image generation using flow matching. We explain and demonstrate why flow matching can generate hallucinations, and propose an iterative process to improve the generation process. Our iterative process can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 31 of its 50 references that are in the map. First posted on arXiv 23 Feb 2025.

Builds on

31 of 50 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Flow Matching Guide and Code9 Dec
  2. 2024Flow matching achieves almost minimax optimal convergence31 May
  3. 2023Flow Matching in Latent Space17 Jul
  4. 2023Multisample Flow Matching: Straightening Flows with Minibatch Couplings28 Apr
  5. 2022Flow Matching for Generative Modeling6 Oct

Other references · 26

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  4. 2022Classifier-Free Diffusion Guidancecited by 5,847
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  6. 2013Auto-Encoding Variational Bayescited by 4,053
  7. 2018Neural Ordinary Differential Equationscited by 3,779
  8. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  9. 2015Variational Inference with Normalizing Flowscited by 2,170
  10. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  11. 2019An Introduction to Variational Autoencoderscited by 1,000
  12. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 939
14 more
  1. 2021Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modelingcited by 602
  2. 2017Stable Architectures for Deep Neural Networkscited by 341
  3. 2019Effectively Unbiased FID and Inception Score and where to find themcited by 165
  4. 2023Diffusion Schr"odinger Bridge Matchingcited by 153
  5. 2021An Introduction to Deep Generative Modelingcited by 104
  6. 2021Deep Generative Learning via Schr"odinger Bridgecited by 104
  7. 2020Composing Normalizing Flows for Inverse Problemscited by 48
  8. 2020Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inferencecited by 42
  9. 2025Memorization and Regularization in Generative Diffusion Modelscited by 40
  10. 2020Sliced Iterative Normalizing Flowscited by 38
  11. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  12. 2020Gaussianization Flowscited by 20
  13. 2023Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-artcited by 14
  14. 2024Diffeomorphic Measure Matching with Kernels for Generative Modelingcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Towards High-Order Mean Flow Generative Models: Feasibility, Expressivity, and Provably Efficient Criteriacited by 5
  2. 2026FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matchingcited by 4
  3. 2026FastFlow: Accelerating The Generative Flow Matching Models with Bandit Inferencecited by 4
  4. 2026Contextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Bellman Optimal Stepsize Straightening of Flow-Matching Models
  2. 2026Perceptual Flow Matching for Few-Step Generative Modeling
  3. 2024Self-Corrected Flow Distillation for Consistent One-Step and Few-Step Text-to-Image Generation
  4. 2025Source-Guided Flow Matching
  5. 2023Straighter Flow Matching via a Diffusion-Based Coupling Prior
  6. 2026Laplacian Multi-scale Flow Matching for Generative Modeling
  7. 2025Align Your Flow: Scaling Continuous-Time Flow Map Distillation
  8. 2025LeDiFlow: Learned Distribution-guided Flow Matching to Accelerate Image Generation