The Coupling Within: Flow Matching via Distilled Normalizing Flows
📌 核心贡献
传统的流模型训练时，噪声和数据怎么配对很重要。以前是独立配对，后来发现根据分布自适应配对（比如用最优传输）效果更好。这篇论文更进一步，不再直接计算这些配对，而是找个「老司机」（预训练模型）来教，用它「蒸馏」出来的配对方式来指导流模型的训练。这就像老司机直接教新人开车，比新人自己摸索路线效率高多了。
📖 摘要
Flow models have rapidly become the go-to method for training and deploying large-scale generators, owing their success to inference-time flexibility via adjustable integration steps. A crucial ingredient in flow training is the choice of coupling measure for sampling noise/data pairs that define the flow matching (FM) regression loss. While FM training defaults usually to independent coupling, recent works show that adaptive couplings informed by noise/data distributions (e.g., via optimal transport, OT) improve both model training and inference. We radicalize this insight by shifting the paradigm: rather than computing adaptive couplings directly, we use distilled couplings from a different, pretrained model capable of placing noise and data spaces in bijection — a property intrinsic to normalizing flows (NF) through their maximum likelihood and invertibility requirements. Leveraging recent advances in NF image generation via auto-regressive (AR) blocks, we propose Normalized Flow Matching (NFM), a new method that distills the quasi-deterministic coupling of pretrained NF models to train student flow models. These students achieve the best of both worlds: significantly outperforming flow models trained with independent or even OT couplings, while also improving on the teacher AR-NF model.
🔍 关键信息
| 字段 | 内容 |
|---|---|
| 发表 | 2026-03-09 |
| 机构 | |
| 分类 | cs.LG, cs.CV |
| 链接 | arXiv |