English(EN) Diversity Without Labels: Task-Agnostic Coreset Selection Across Perception and Action

CoreDNA方法为AI模型训练提供无监督核选择

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

研究人员开发了CoreDNA，一种新颖的无监督方法，用于选择高质量的数据集进行模型训练。这种任务无关的方法通过分析基础模型的神经元激活来识别代表性数据样本，而无需标签或代理模型训练。CoreDNA能有效捕捉低级视觉和高级语义信息，在图像分类、分割和模仿学习等各种任务中均优于现有的最先进方法。 AI

影响该方法可以简化AI模型的数据集 curation，有可能降低标注成本并提高各种AI应用的训练效率。

排序理由该集群包含一篇详细介绍新核选择方法的 ist 研究论文。

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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arXiv cs.CV TIER_1 English(EN) · Julia Hindel, Benjamin Ramtoula, Kyuhwan Yeon, Nick Heppert, Paul Newman, Daniele De Martini, Abhinav Valada · 2026-10-19 04:00

无标签多样性：跨感知与行动的任务无关核心集选择

arXiv:2610.23642v1 Announce Type: new Abstract: While data collection for model training is relatively easy, curating high-quality datasets from large collections is inherently challenging. Coreset estimation methods address this problem by identifying the best data subsets to tr…