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English(EN) Neural Collapse on Imbalanced Datasets and Open-Set Scenario

神经坍塌理论针对不平衡和开放集数据集的重新表述

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员对神经坍塌（NC）进行了理论上的重新表述，以适应不平衡数据集和开放集场景。他们的分析基于类平均损失而非全局平均损失，表明即使在数据不平衡的情况下，对于交叉熵和二元交叉熵损失，当这些类平均损失达到最小值时，标准NC即可实现。该研究进一步将此扩展到开放集情况，表明未知类的分类器向量在最小损失条件下会重叠并与已知类正交。 AI

影响 为理解模型在具有挑战性的真实数据分布上的行为提供了理论框架。

排序理由 阐述机器学习概念理论进展的学术论文。[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 →

神经坍塌理论针对不平衡和开放集数据集的重新表述

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阐述机器学习概念理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qiufu Li, Huibing Xiao, Linlin Shen, Yongdong Huang ·

    不平衡数据集和开放集场景下的神经坍塌

    arXiv:2611.07753v1 Announce Type: new Abstract: Neural collapse (NC) describes the elegant geometric structure that sample features and classifier vectors exhibit when a classification model's loss reaches its minimum. Class imbalance, however, generally distorts standard NC. In …

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