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English(EN) A Strong Balanced-Softmax Classifier-Retraining Baseline for Long-Tailed Recognition

新的BS-cRT方法提高了长尾识别的准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为BS-cRT的新型长尾识别基线方法，旨在通过在初始训练后仅重新训练分类器来提高准确性。该两阶段过程包括使用平衡Softmax训练骨干网络和余弦分类器，然后冻结骨干网络，仅在平衡的样本批次上更新分类器。BS-cRT方法在CIFAR-100-LT、CIFAR-10-LT、ImageNet-LT和Places-LT等各种数据集上持续提高少样本准确性，尤其是在较高的不平衡因子下显示出显著的提升。 AI

影响 这项研究为提高AI识别数据集中稀有类别的能力提供了一个实用的基线，有望在数据不平衡的实际应用中提升性能。

排序理由 该集群包含一篇详细介绍机器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BS-cRT方法提高了长尾识别的准确性

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该集群包含一篇详细介绍机器学习新方法的学术论文。[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) · Juan Terven, Diana Margarita C\'ordova Esparza, Julio Alejandro Romero Gonzalez, Edgar Arturo Ch\'avez Urbiola, Francisco Javier Willars Rodriguez, Juan Bautista Hurtado Ramos, Alfonso Ramirez Pedraza ·

    长尾识别的强平衡Softmax分类器再训练基线

    arXiv:2607.09832v1 Announce Type: new Abstract: Long-tailed recognition methods often modify losses, margins, or representations to reduce the dominance of frequent classes. We ask whether, after Balanced Softmax training, the remaining tail error can be reduced by retraining onl…

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