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SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight

Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becomes the natural solution to cross-user WHAR under annotation scarcity.

Data and Resources

Cite this as

Rong Hu, Ling Chen, Shenghuan Miao, Xing Tang (2024). Dataset: SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight. https://doi.org/10.57702/p2rpehwy

DOI retrieved: December 3, 2024

Additional Info

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Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2212.00724
Author Rong Hu
More Authors
Ling Chen
Shenghuan Miao
Xing Tang
Homepage https://github.com/Rxannro/SWL-Adapt