NDLI: Adaptive Convolutional Neural Network and Its Application in Face Recognition

Content Provider	Springer Nature Link
Author	Zhang, Yuanyuan Zhao, Dong Sun, Jiande Zou, Guofeng Li, Wentao
Copyright Year	2015
Abstract	Convolutional neural network (CNN) has more and more applications in image recognition. However, the structure of CNN is often determined after a performance comparison among the CNNs with different structures, which impedes the further development of CNN. In this paper, an adaptive convolutional neural network (ACNN) is proposed, which can determine the structure of CNN without performance comparison. The final structure of ACNN is determined by automatic expansion according to performance requirement. First, the network is initialized by a one-branch structure. The system average error and recognition rate of the training samples are set to control the expansion of the structure of CNN. That is to say, the network is extended by global expansion until the system average error meets the requirement and when the system average error is satisfied, the local network is expanded until the recognition rate meets the requirement. Finally, the structure of CNN is determined automatically. Besides, the incremental learning for new samples can be achieved by adding new branches while keeping the original network unchanged. The experiment results of face recognition on ORL face database show that there is a better tradeoff between the consumption of training time and the recognition rate in ACNN.
Starting Page	389
Ending Page	399
Page Count	11
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	43
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2015-03-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Convolutional neural network Network construction Adaptive convolutional neural network Global expansion Local expansion Incremental learning Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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