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English(EN) Mini-batch Sampling Strategies for Long-Tailed Image Classification: An Empirical Study on CIFAR-100-LT

新研究比较长尾图像分类的最小批次采样策略

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新的arXiv研究调查了长尾图像分类任务的最小批次采样策略，重点关注CIFAR-100-LT数据集。研究人员使用ResNet-32比较了均匀实例采样、类别平衡采样、平方根采样和渐进平衡采样。研究结果表明，渐进采样显著提高了尾部类别的准确性，在高不平衡率下比均匀基线相对提高了25%，同时没有损害整体准确性。 AI

影响 为优化具有不平衡类别分布的数据集的训练提供了见解，这对于现实世界的AI应用至关重要。

排序理由 关于机器学习方法学的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新研究比较长尾图像分类的最小批次采样策略

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关于机器学习方法学的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Siyu Yuan ·

    长尾图像分类的最小批量采样策略：CIFAR-100-LT上的实证研究

    arXiv:2609.16365v1 Announce Type: new Abstract: Real-world datasets often exhibit long-tailed class distributions, where a few head classes contain a large number of training samples while a large number of tail classes have only a few. The composition of each mini-batch, determi…

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