The Generation Phases of Flow Matching: a Denoising Perspective

2025-10-28

AI Summary

Existing studies lack a systematic understanding of the key mechanisms underlying generation quality in Flow Matching (FM), particularly regarding the causes of denoiser success or failure across distinct dynamical stages of the generative process. Method: We establish a theoretical analysis framework from a denoising perspective, explicitly linking FM models to denoiser performance and identifying characteristic dynamical phases during generation. Through controlled experiments with noise and drift perturbations, we empirically characterize how noise structure and drift terms differentially affect denoising capability across phases. Contribution/Results: We首次 reveal a phase-dependent pattern of denoising performance—exhibiting sequential degradation and recovery across the generation trajectory—and propose a verifiable dynamical diagnostic methodology. Our work provides a novel mechanistic interpretation pathway and a quantifiable analytical paradigm for diffusion-based generative models.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Flow matching has achieved remarkable success, yet the factors influencing the quality of its generation process remain poorly understood. In this work, we adopt a denoising perspective and design a framework to empirically probe the generation process. Laying down the formal connections between flow matching models and denoisers, we provide a common ground to compare their performances on generation and denoising. This enables the design of principled and controlled perturbations to influence sample generation: noise and drift. This leads to new insights on the distinct dynamical phases of the generative process, enabling us to precisely characterize at which stage of the generative process denoisers succeed or fail and why this matters.

Problem

• Understanding factors affecting flow matching generation quality
• Connecting flow matching models with denoisers through empirical framework
• Characterizing denoiser performance across generative process phases

Innovation

• Framework connects flow matching with denoisers
• Perturbations influence generation via noise and drift
• Characterizes denoiser success phases in generation process