Application in Activity Recognition Data Augmentation with Modified Generative Adversarial Networks

  • Xiao Jian, Yingying Peng

Abstract

Deep learning (DL)is a new research hotspot in the artificial intelligence community. However, the most important thing for deep learning is that it requires a lot of labeled data as support. But the process of data collecting often requires a lot of hard work in order to ensure that the data is correct enough to be used. Our study focuses on activity recognition, using modified conditional generative adversarial network, and augmented data with original data to build a dataset with similar features. Comparing with the data augmentation use of generative adversarial network and the original data, its generalization ability and accuracy have been similar with original dataset’s result.

Published
2020-02-29
How to Cite
Xiao Jian, Yingying Peng. (2020). Application in Activity Recognition Data Augmentation with Modified Generative Adversarial Networks. Design Engineering, 596 - 609. https://doi.org/10.17762/de.vi.280
Section
Articles