한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제21권1호
  • /
  • Pages.1-8
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  • 2016
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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New Approach to Optimize the Size of Convolution Mask in Convolutional Neural Networks

  • Kwak, Young-Tae (Dept. of IT and Engineering, Chonbuk National University)
  • 투고 : 2015.11.03
  • 심사 : 2015.12.30
  • 발행 : 2016.01.30
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초록

Convolutional neural network (CNN) consists of a few pairs of both convolution layer and subsampling layer. Thus it has more hidden layers than multi-layer perceptron. With the increased layers, the size of convolution mask ultimately determines the total number of weights in CNN because the mask is shared among input images. It also is an important learning factor which makes or breaks CNN's learning. Therefore, this paper proposes the best method to choose the convolution size and the number of layers for learning CNN successfully. Through our face recognition with vast learning examples, we found that the best size of convolution mask is 5 by 5 and 7 by 7, regardless of the number of layers. In addition, the CNN with two pairs of both convolution and subsampling layer is found to make the best performance as if the multi-layer perceptron having two hidden layers does.

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참고문헌

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