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Proceedings Paper

Improvement of semi-supervised learning in real application scenarios
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Paper Abstract

Due to the high demand of deep learning for data quantity, semi-supervised learning (SSL) has a very important application prospect because of its successful use of unlabeled data. Existing SSL algorithms have achieved high accuracy on MINIST, CIFAR-10 and SHVN datasets, and even outperform fully supervised algorithms. However, because the above three datasets have the characteristics of balanced data categories and simple identification tasks which can’t be ignored for classification problems, the SSL algorithm has uncertainties of effectiveness in the case of unbalanced datasets and specific recognition tasks. We analyze the datasets and find that the number of “disgust” in expressions dataset is less than other categories, and so is “discussion” in the classroom action recognition dataset. Therefore, we use a novel SSL model: Deep Co-Training (DCT) model to experiment on the expression recognition database (FER2013), as well as our own classroom student action database (BNU-LCSAD) and analyze the effectiveness of the algorithm in specific application scenarios. Moreover, we use a training strategy of TSA when train our model to solve the problem of being easily overfitting which is more likely to occur when data categories are not balanced. The experimental results prove the effectiveness of the SSL algorithm in practical application and the significance of using TSA.

Paper Details

Date Published: 18 November 2019
PDF: 7 pages
Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111871W (18 November 2019); doi: 10.1117/12.2539055
Show Author Affiliations
Jun He, Beijing Normal Univ. (China)
Huanqing Yan, Beijing Normal Univ. (China)
Yongkang Xiao, Beijing Normal Univ. (China)
Bo Sun, Beijing Normal Univ. (China)
Lejun Yu, Beijing Normal Univ. (China)
Kaijie Zhao, Beijing Normal Univ. (China)

Published in SPIE Proceedings Vol. 11187:
Optoelectronic Imaging and Multimedia Technology VI
Qionghai Dai; Tsutomu Shimura; Zhenrong Zheng, Editor(s)

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