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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| Published in | Neurocomputing (Amsterdam) Vol. 500; pp. 649 - 661 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier
01.08.2022
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| Subjects | |
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| Author | Amirat, Yacine Chibani, Abdelghani Soleimani, Elnaz Khodabandelou, Ghazaleh |
|---|---|
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| Subtitle | 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 |
| Title | Generic semi-supervised adversarial subject translation for sensor-based activity recognition |
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