English(EN) COFM: Consistent Optimal Transport Flow Matching via Partially Input Convex Neural Networks

新的COFM框架通过PICNN增强最优传输流匹配

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员推出了一种新颖的一致最优传输流匹配框架COFM。该方法利用部分输入凸神经网络（PICNNs），并结合汉密尔顿-雅可比残差以确保动力学一致性。COFM能够实现高效的一步传输和基于ODE的多步采样，而无需昂贵的内部优化。实验表明，与现有的最先进模型相比，COFM在性能上具有竞争力，并且计算效率显著。 AI

影响引入了一种更高效、更一致的传输学习方法，可能对生成模型和科学计算应用产生影响。

排序理由该集群包含一篇详细介绍最优传输流匹配新方法的论文。[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) · Fanghui Song, Zhongjian Wang, Jiebao Sun · 2026-08-28 04:00

COFM：通过部分输入凸神经网络实现一致的最优传输流匹配

arXiv:2511.06042v2 Announce Type: replace Abstract: Optimal transport (OT) provides a principled framework for learning mappings between probability distributions, and has found broad applications in generative modeling, inverse problems and scientific computing. Recently, flow m…

本文如何被排名

报道来源 [1]

COFM：通过部分输入凸神经网络实现一致的最优传输流匹配

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