Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 166-171.doi: 10.11896/JsJkx.190600179

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Application of Multi-scale Dilated Convolution in Image Classification

WU Hao-hao and WANG Fang-shi   

  1. School of Software,BeiJing Jiaotong University,BeiJing 100044,China
  • Published:2020-07-07
  • About author:WU Hao-hao, born in 1995, master.His main research interests include deeplearning and so on.
    WANG Fang-shi, born in 1969, Ph.D, professor, is a member of China Computer Federation.Her main research interests include visual information processing and pattern recognition.

Abstract: In order to reduce the loss of spatial information caused by down sampling,dilated convolution is often used instead of down-sampling in image classification based on deep learning.However,there is no literature on the performance difference of dilated convolution on different network layers.In this paper,a large number of image classification experiments have been carried out,and the best network layer suitable for dilated convolution has been found.However,the use of dilated convolution will lose the information of neighboring points,resulting in grid phenomenon and the loss of partial information of the image.In order to eliminate the grid phenomenon,this paper also proposes a method of constructing neural network by using multi-scale dilated convolution in the optimal network layer mentioned above.The experimental results show that the proposed network construction method achieves good results in image classification.

Key words: Dilated convolution, Image classification, Multi-scale, Neural network

CLC Number: 

  • TP301
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