Leveraging Artificial Occluded Samples for Data Augmentation in Human Activity Recognition
A significant challenge in human activity recognition lies in the limited size and diversity of training datasets, which can lead to overfitting and the poor generalization of deep learning models. Common solutions include data augmentation and transfer learning. This paper introduces a novel data a...
| Päätekijät: | , , , |
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| Aineistotyyppi: | Artikkeli |
| Kieli: | English |
| Julkaistu: |
MDPI AG
2025-02-01
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| Sarja: | Sensors |
| Aiheet: | |
| Linkit: | https://www.mdpi.com/1424-8220/25/4/1163 |