English(EN) An Analysis of Self-supervised Pre-training with Dependent Samples

自监督学习论文表明，池化增强优于数据划分

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

Maximilian Fleissner 在 arXiv 上发表的一篇新论文分析了自监督预训练方法的有效性，特别关注相关数据增强的池化。研究表明，尽管池化增强存在相互依赖性，但与将数据划分为独立子集相比，它能产生更好的统计估计误差界限。这种池化方法可以提高学习速度并降低估计方差，从而为实际自监督学习为何通常倾向于使用大量增强提供了新的见解。 AI

影响为池化增强样本在自监督预训练中的实际成功提供了理论见解。

排序理由发布在 arXiv 上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Maximilian Fleissner, Debarghya Ghoshdastidar, Samory Kpotufe · 2026-09-07 04:00

含相关样本的自监督预训练分析

arXiv:2609.05031v1 Announce Type: cross Abstract: Self-supervised learning relies on so-called data augmentations $\phi(x)$ of unlabeled datapoints $x$ --- for example, masking random pixels in an image $x$ --- that should leave the label of $x$ invariant and are often used to le…

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含相关样本的自监督预训练分析

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