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English(EN) Low-Budget Active Learning through Entropic Optimal Transport

新的主动学习方法使用熵最优传输进行低预算数据选择

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的低预算主动学习方法，该方法侧重于选择一小部分代表性数据子集（核心集）用于训练模型。这种方法在医学等数据标注成本高昂的领域特别有用。该方法利用了预训练的自监督模型的特征，并采用熵最优传输（特别是Sinkhorn散度）作为选择标准。该技术对解决方案质量提供了理论保证，并允许高效计算，在图像和医学数据集的低预算场景下优于现有启发式方法。 AI

影响 该方法可以降低医学等数据稀缺领域训练AI模型的成本。

排序理由 该集群包含一篇详细介绍主动学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新的主动学习方法使用熵最优传输进行低预算数据选择

本文如何被排名

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Tool
该集群包含一篇详细介绍主动学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rim Hajal, Mathieu Besan\c{c}on, J\'er\^ome Malick ·

    低成本主动学习通过熵最优传输

    arXiv:2610.01199v1 Announce Type: new Abstract: We consider low-budget active learning, which consists of selecting a limited number of points, the coreset, such that a model can be trained to high accuracy on the selection only. This problem is particularly relevant in contexts …

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