Generic semi-supervised adversarial subject translation for sensor-based activity recognition Performance of Human Activity Recognition (HAR) models, particularly deep neural networks, is highly contingent upon the availability of the massive amount of annotated training data. Though, data collection and manual labeling in the HAR domain are prohibitively expensive due to human resource dependence in both steps. Hence, domain adaptation techniques are proposed to adapt the knowledge from the existing source of data. More recently, adversarial transfer learning methods have shown promisin

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 500; pp. 649 - 661
Main Authors Soleimani, Elnaz, Khodabandelou, Ghazaleh, Chibani, Abdelghani, Amirat, Yacine
Format Journal Article
LanguageEnglish
Published Elsevier 01.08.2022
Subjects
Artificial Intelligence
Computer Science
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ISSN:0925-2312
DOI:10.1016/j.neucom.2022.05.075