English(EN) SCALE: Improving Guidance-Free Visual Generation via Self-Contrastive Alignment for Likelihood Estimation

新的SCALE框架改进了无分类器引导的视觉生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员引入了SCALE，一个新颖的训练框架，旨在提高视觉生成质量，同时避免了传统分类器无引导（CFG）的计算开销。SCALE利用自对比信号，比较真实样本和生成样本上的预测，以更有效地指导生成过程。该方法与似然目标相结合，在单次前向传播中产生增强的预测，优于现有的无引导技术，并在与直接偏好优化（DPO）等基于偏好的微调方法结合时显示出互补的优势。 AI

影响通过降低与传统引导方法相关的计算成本，提供了一种更高效的视觉生成方法。

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

在 arXiv cs.CV 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.CV TIER_1 English(EN) · Jiwan Hur, Dong-Jae Lee, Dongyeun Lee, Gyojin Han, Junmo Kim · 2026-10-19 04:00

SCALE：通过自对比对齐以改进无指导视觉生成和似然估计

arXiv:2610.23263v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is essential for improving sample quality in visual generation, but it incurs substantial sampling overhead. Existing guidance-free approaches halve this cost by approximating the CFG with a single mod…

本文如何被排名

报道来源 [1]

SCALE：通过自对比对齐以改进无指导视觉生成和似然估计

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