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English(EN) Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels

离散均流在离散状态空间中实现一步生成

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了离散均流（Discrete MeanFlow），一个用于离散状态空间一步生成的新型理论框架。该方法用概率质量传输取代了均流的连续速度场概念，利用连续时间马尔可夫链的条件转移核。离散均流建立了一个将有限区间速率与瞬时马尔可夫链生成器相关联的恒等式，从而可以直接参数化转移核。该方法允许通过单次前向传播和直接采样进行生成，绕过了迭代去噪或多步精炼过程。 AI

影响 引入了离散状态空间生成的新型理论框架，通过实现一步采样可能提高效率。

排序理由 该集群描述了arXiv论文中提出的新理论框架和恒等式。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

离散均流在离散状态空间中实现一步生成

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该集群描述了arXiv论文中提出的新理论框架和恒等式。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed, Ruiquan Huang, Peizhong Ju ·

    离散均流：通过条件转移核实现单步生成

    arXiv:2605.12805v2 Announce Type: replace-cross Abstract: MeanFlow enables one-step generation in continuous spaces by learning an average velocity over a time interval rather than the instantaneous velocity field of flow matching. However, discrete state spaces lack smooth traje…

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