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English(EN) Partial FC: Training 10 Million Identities on a Single Machine

部分FC方法可实现千万级身份人脸识别模型训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为部分FC（PFC）的新颖方法，可在单台机器上高效训练具有数百万身份的人脸识别模型。该技术通过在每个小批量中仅激活采样的一小部分负类中心来近似完整的softmax分类器，同时仍保留所有正类中心。PFC显著降低了内存、计算和通信开销，能够以具有竞争力的准确性和更高的效率进行大规模训练，支持数千万个类别。该方法还对标签噪声和长尾身份分布表现出鲁棒性。 AI

影响 该方法可以显著降低训练大规模人脸识别系统的计算成本和硬件要求。

排序理由 该集群包含一篇详细介绍模型训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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部分FC方法可实现千万级身份人脸识别模型训练

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该集群包含一篇详细介绍模型训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xiang An, Xuhan Zhu, Yang Xiao, Lan Wu, Ming Zhang, Yuan Gao, Bin Qin, Debing Zhang, Ying Fu, Jiankang Deng ·

    Partial FC：单机训练千万身份

    arXiv:2010.05222v3 Announce Type: replace Abstract: Training face recognition models with millions of identities is challenging because classifier storage, logit memory, and computation grow linearly with the number of classes, eventually making full softmax impractical even when…

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