Technology of Graphic & Image
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1870-1873

Improvement of batch normalization:BNalpha

Luo Chenhui
Sun Hongfei
School of Aerospace Engineering, Xiamen University, Xiamen Fujian 361101, China

Abstract

In order to improve the training speed and certain classification accuracy of CNN in the image classification, this paper started from BN, adjusted the affine transform of BN by adding new parameters, and proposed an improved BN, called BNalpha. Except for the neural networks with some specific structures, compared with the original BN, BNalpha could improve the training speed and certain classification accuracy of general neural networks without increasing the computational complexity. By analyzing and comparing the parameters of BN affine transform, this paper tried to explain the operation me-chanism of BN partially, and conducted contrast experiment which covering different periods during training to illustrate that BNalpha was superior to the original BN. Based on the CIFAR-10 and CIFAR-100 datasets, by using various types of CNN structures, it compared and analyzed BNalpha and BN, and the experimental results verify that BNalpha can further improve the training speed and certain classification accuracy.

Foundation Support

国家自然科学基金资助项目(61273153,61374037)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.06.0202
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 6
Section: Technology of Graphic & Image
Pages: 1870-1873
Serial Number: 1001-3695(2021)06-051-1870-04

Publish History

[2021-06-05] Printed Article

Cite This Article

罗晨辉, 孙洪飞. 改进型的batch normalization:BNalpha [J]. 计算机应用研究, 2021, 38 (6): 1870-1873. (Luo Chenhui, Sun Hongfei. Improvement of batch normalization:BNalpha [J]. Application Research of Computers, 2021, 38 (6): 1870-1873. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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