{"version":"1.0","generated_at":"2026-10-04T17:38:07.079085","id":1844,"slug":"towards-demystifying-the-creativity-of-diffusion-models","title":"解密扩散模型的创造力","summary":"研究表明，扩散模型能够生成新颖数据而非简单记忆训练集，其创造力源于神经网络学习分数函数时的自然平滑效应，导致模型在隐藏数据流形上的训练点之间进行插值。","abstract":"解密扩散模型的创造力 研究表明，扩散模型能够生成新颖数据而非简单记忆训练集，其创造力源于神经网络学习分数函数时的自然平滑效应，导致模型在隐藏数据流形上的训练点之间进行插值。 研究表明，扩散模型能够生成新颖数据而非简单记忆训练集，其创造力源于神经网络学习分数函数时的自然平滑效应，导致模型在隐藏数据流形上的训练点之间进行插值。 原贴提到：Zhengdao Chen, Research Scientist, Google Research We show that a diffus 来源：research.google Zhengdao Chen, Research Scientist, Google Research 我们证明了扩散模型的创造力（即生成新颖数据而非仅记忆训练集的能力）是神经网络学习分数函数“平滑”版本的数学结果，这驱使模型沿着隐藏数据流形在训练数据点之间进行插值。当前，扩散模型是生成具有复杂局部结构的数据（如图像生成和分子发现）的最强大工具之一。它们展现出超越训练数据泛化的令人兴奋的能力，并在此意义上表现出“创造力”。例如，在通过真实图像数据集训练后，它们能将随机噪声样本转化为新颖的高质量图像。这种创造能力令人印象深刻，但引出一个有趣的问题：它从何而来？理解这个问题的答案是揭开基于扩散的生成式AI“黑箱”本质的重要一步。为此，在发表于ICLR 2026的论文《On the Interpolation Effect of Score Smoothing in Diffusion Models》中，我们深入扩散模型的数学以回答此问题。我们表明，模型的创造力并非随机偶然…","access_level":"member","access_label":"免费阅读","access_mode":"full","is_preview":false,"canonical_url":"https://opc.beizhux.com/content/1844/towards-demystifying-the-creativity-of-diffusion-models","html_url":"https://opc.beizhux.com/content/1844/towards-demystifying-the-creativity-of-diffusion-models","json_url":"https://opc.beizhux.com/content/1844/towards-demystifying-the-creativity-of-diffusion-models.json","published_at":"2026-07-16T02:06:27","updated_at":"2026-10-04T17:01:40","category":{"slug":"ai-business","name":"AI小生意项目库"},"source":{"site":"research.google","author":"Google Research Blog","url":"https://research.google/blog/towards-demystifying-the-creativity-of-diffusion-models/"},"tags":["AI","creativity","diffusion models","Google Research","Google Research Blog","ICLR 2026","interpolation","score 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