English(EN) Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport

新型模型感知调度增强扩散和流匹配生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一种新颖的扩散和流匹配调度构建方法，这对于控制生成模型中数据和噪声的混合至关重要。这种新方法称为“模型感知调度”，它利用纤维最优输运来考虑预测误差，而之前的模型无关方法则不能。该技术涉及基于最优输运成本定义纤维预测风险，并将其与系数路径的动力学作用相结合，以确定最优时间分配。该方法已证明在流匹配CIFAR-10的FID分数方面比强大的基线有持续改进，实现了显著降低。 AI

影响这种新的调度方法可能导致扩散和流匹配模型中更高效、更高质量的生成。

排序理由该集群包含一篇详细介绍生成模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Luyi Jia, Boyan Zhang, Yilun Liu, Steffen Rulands · 2026-09-11 04:00

模型感知调度通过纤维最优传输改进生成

arXiv:2609.11842v1 Announce Type: new Abstract: Diffusion and flow-matching schedules control the signal and noise coefficients that mix data and noise along affine probability paths. Minimizing a kinetic action defined on coefficient paths, motivated by optimal transport, helps …

本文如何被排名

报道来源 [1]

模型感知调度通过纤维最优传输改进生成

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