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Updated: May 31, 2025

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转移和变异:评估HAR深度学习模型对变量的稳定性

Azhar Ali Khaked1, Nobuyuki Oishi2, Daniel Roggen2

  • 1Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
概括

对于人类活动识别的深度学习模型在与真实世界的数据变异性作斗争. 分析主体,设备和方向变化显示了显著的性能下降,突出了对更强大的模型的需求.

关键词:
数据异质性数据异质性深度学习是一种深度学习.分布转移转移分配转移的时间.人类活动的认可 人类活动的认可模型稳定性评估 模型稳定性评估现实世界的变化性.可以穿戴的传感器.

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