English(EN) Controlling Polar Exposure to Delay Memorization in Diffusion Models

新方法延迟扩散模型记忆，延长泛化能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了一种名为质量门控去白化（QGD）的新方法，用于控制扩散模型中训练数据的记忆。该技术旨在延迟模型开始复制训练样本的时间点，从而延长有用泛化的周期。QGD通过保持快速的初始更新阶段，然后逐渐恢复动量来工作，并通过分析将不同类型的记忆分开。在CIFAR-10子集上进行测试时，QGD显著扩展了扩散模型的有用区间，并与标准SGD相比减少了复制，同时还实现了更好的FID分数。 AI

影响改善了扩散模型中的质量-复制权衡，可能带来更强的泛化能力。

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在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xuanchen Wang, Heng Wang, Weidong Cai · 2026-10-05 04:00

控制极性暴露以延迟扩散模型中的记忆

arXiv:2610.02780v1 Announce Type: new Abstract: Diffusion models can reach useful sample quality before copying training examples, but fast optimization can compress this generalization window by accelerating sample-specific fitting. We investigate this effect through update geom…

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控制极性暴露以延迟扩散模型中的记忆

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