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 depe...
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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 B.V
21.08.2022
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Subjects | |
Online Access | Get full text |
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