@inproceedings{5305dffd16954cba9e50c2b2e7fd48f2,
title = "Improvement of pose recognition by sparse regularized convolutional neural network",
abstract = "The paper proposes a method of using deep model (CNN) with sparse regularization term to improve the performance of pose recognition. Convolutional neural network shows its limitations for pose recognition because of its bad convergence. It is believed that the activation function could be simplified by adding the sparseness term. Therefore, we present an algorithm applying the sparse regularization for the deep model with ReLU. Experimental results confirm that the proposed method can accelerate the convergence speed while a high recognition rate maintained.",
author = "Yuan Zhang and Xiao Yao and Zhongli Wang and Hao Su and Zihan Yu and Guanying Huo and Ning Xu and Xiaofeng Liu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019 ; Conference date: 04-08-2019 Through 09-08-2019",
year = "2019",
month = aug,
doi = "10.1109/RCAR47638.2019.9044112",
language = "英语",
series = "2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "248--251",
booktitle = "2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019",
address = "美国",
}