You're currently viewing an old version of this dataset. To see the current version, click here.

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

This dataset has no data

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

Private DOI This DOI is not yet resolvable.
It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

Field	Value
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