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English(EN) From Perturbation Correction to Geometry-Aware Sampling: Sharpness-Guided Equilibrium Sampling for Balanced Flat Minima in Long-Tailed Learning

新采样方法提升长尾学习准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为锐度引导均衡采样（SGS）的新方法，以提高在长尾数据集上训练的模型的性能。SGS在训练过程中动态调整数据点的采样概率，优先考虑代表性不足的类别，同时在锐度感知最小化（SAM）使用时降低那些导致损失景观显著变化的类别权重。该方法旨在实现更平衡、更平坦的损失景观，从而提高泛化能力。SGS-SAM在CIFAR-100 LT和ImageNet-LT基准测试中表现出显著的改进，尾部准确率最高提升10.85个百分点，整体准确率提升3.56个百分点，而训练时间仅略有增加。 AI

影响 增强了模型在类别分布不均数据集上的泛化能力，这对于实际应用至关重要。

排序理由 学术论文，详细介绍了改进不平衡数据集上机器学习的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新采样方法提升长尾学习准确性

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学术论文，详细介绍了改进不平衡数据集上机器学习的新方法。[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) · Jiaxin Deng, Junbiao Pang ·

    从扰动校正到几何感知采样：锐度引导的平衡采样用于长尾学习中的平衡平坦最小值

    arXiv:2607.21999v1 Announce Type: new Abstract: Long-tailed learning couples two sources of poor generalization: head classes dominate training exposure, while under-represented classes often converge to sharper regions of the loss landscape. Conventional re-sampling addresses th…

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