统一框架解析扩散与流匹配，揭示模型鲁棒性来源
Exploring Diffusion and Flow Matching Under Generator Matching
- 将扩散与流匹配统一到生成马尔可夫框架下
- 解释流匹配为何更鲁棒，且可混合确定与随机组件
- 为构建新生成模型提供理论依据，适合生成模型研究者
本文在生成匹配框架下，对扩散模型与流匹配进行了全面的理论比较。尽管二者表面差异明显，但在统一的生成匹配框架下可被统一看待。通过将两者重新置于相同的生成马尔可夫框架中，我们揭示了为何流匹配模型在实际中更具鲁棒性，并提出了通过混合确定性与随机组件构造新型模型类的方法。该分析为当前最先进的生成建模范式之间的关系提供了新的视角。
原文摘要 · Abstract (English)
In this paper, we present a comprehensive theoretical comparison of diffusion and flow matching under the Generator Matching framework. Despite their apparent differences, both diffusion and flow matching can be viewed under the unified framework of Generator Matching. By recasting both diffusion and flow matching under the same generative Markov framework, we provide theoretical insights into why flow matching models can be more robust empirically and how novel model classes can be constructed by mixing deterministic and stochastic components. Our analysis offers a fresh perspective on the relationships between state-of-the-art generative modeling paradigms.
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