ContrasGAN: Unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning

2021 ◽  
pp. 101477
Author(s):  
Andrea Rosales Sanabria ◽  
Franco Zambonelli ◽  
Simon Dobson ◽  
Juan Ye
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Domain Adaptation ◽  
Human Activity Recognition ◽  
Unsupervised Domain Adaptation

AdaptNet: Human Activity Recognition via Bilateral Domain Adaptation using Semi-Supervised Deep Translation Networks

2021 ◽  
pp. 1-1
Author(s):  
Sungtae An ◽  
Alessio Medda ◽  
Michael N. Sawka ◽  
Clayton J. Hutto ◽  
Mindy L. Millard-Stafford ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Domain Adaptation ◽  
Human Activity Recognition

scholarly journals Data Augmentation vs. Domain Adaptation—A Case Study in Human Activity Recognition

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 55
Author(s):  
Evaggelos Spyrou ◽  
Eirini Mathe ◽  
Georgios Pikramenos ◽  
Konstantinos Kechagias ◽  
Phivos Mylonas
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Recognition Task ◽  
Human Activity Recognition ◽  
Video Data ◽  
Training Dataset ◽  
Data Systems ◽  
Big Data Systems

Recent advances in big data systems and databases have made it possible to gather raw unlabeled data at unprecedented rates. However, labeling such data constitutes a costly and timely process. This is especially true for video data, and in particular for human activity recognition (HAR) tasks. For this reason, methods for reducing the need of labeled data for HAR applications have drawn significant attention from the research community. In particular, two popular approaches developed to address the above issue are data augmentation and domain adaptation. The former attempts to leverage problem-specific, hand-crafted data synthesizers to augment the training dataset with artificial labeled data instances. The latter attempts to extract knowledge from distinct but related supervised learning tasks for which labeled data is more abundant than the problem at hand. Both methods have been extensively studied and used successfully on various tasks, but a comprehensive comparison of the two has not been carried out in the context of video data HAR. In this work, we fill this gap by providing ample experimental results comparing data augmentation and domain adaptation techniques on a cross-viewpoint, human activity recognition task from pose information.

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