mlsanityData to 14 Oct 2026

Correlation Analysis of Generative Models

arXiv 2603.06614 ↗First posted 20 Feb 2026cs.LG cs.CVcited by 0

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

Abstract (arXiv)

Based on literature review about existing diffusion models and flow matching with a neural network to predict a predefined target from noisy data, a unified representation is first proposed for these models using two simple linear equations in this paper. Theoretical analysis of the proposed model is then presented. Our theoretical analysis shows that the correlation between the noisy data and the predicted target is sometimes weak in the existing diffusion models and flow matching. This might affect the prediction (or learning) process which plays a crucial role in all models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 24 of its 30 references that are in the map. First posted on arXiv 20 Feb 2026.

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24 of 30 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Straighten Viscous Rectified Flow via Noise Optimization14 Jul
  2. 2024Physics-Informed Diffusion Models21 Mar
  3. 2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation12 Sep
  4. 2022Flow Matching for Generative Modeling6 Oct
  5. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow7 Sep

Other references · 19

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
  6. 2018Neural Ordinary Differential Equationscited by 3,779
  7. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
  8. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  9. 2024π0: A Vision-Language-Action Flow Model for General Robot Controlcited by 2,407
  10. 2023Consistency Modelscited by 1,822
  11. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  12. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 774
7 more
  1. 2024Simplifying, Stabilizing and Scaling Continuous-Time Consistency Modelscited by 283
  2. 2025SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillationcited by 87
  3. 2025Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulationcited by 24
  4. 2024DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulationcited by 15
  5. 2023MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Modelcited by 13
  6. 2025FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulationcited by 13
  7. 2025EQ-TAA: Equivariant Traffic Accident Anticipation via Diffusion-Based Accident Video Synthesiscited by 2

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  1. 2025An Introduction to Flow Matching and Diffusion Models
  2. 2024Explicit Flow Matching: On The Theory of Flow Matching Algorithms with Applications
  3. 2026A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models
  4. 2025Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching
  5. 2024Exploring Diffusion and Flow Matching Under Generator Matching
  6. 2025A Theoretical Analysis of Discrete Flow Matching Generative Models
  7. 2024Theoretical research on generative diffusion models: an overview
  8. 2022From Points to Functions: Infinite-dimensional Representations in Diffusion Models