APA (7th ed.) Citation

Soleimani, E., Khodabandelou, G., Chibani, A., & Amirat, Y. (2022). 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. Neurocomputing (Amsterdam), 500, 649-661. https://doi.org/10.1016/j.neucom.2022.05.075

Chicago Style (17th ed.) Citation

Soleimani, Elnaz, Ghazaleh Khodabandelou, Abdelghani Chibani, and Yacine Amirat. "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." Neurocomputing (Amsterdam) 500 (2022): 649-661. https://doi.org/10.1016/j.neucom.2022.05.075.

MLA (9th ed.) Citation

Soleimani, Elnaz, et al. "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." Neurocomputing (Amsterdam), vol. 500, 2022, pp. 649-661, https://doi.org/10.1016/j.neucom.2022.05.075.