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Channel Max Pooling for Image Classification

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Intelligence Science and Big Data Engineering. Visual Data Engineering (IScIDE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11935))

Abstract

A problem of deep convolutional neural networks is that the channel numbers of the feature maps often increases with the depth of the network. This problem can result in a dramatic increase in the number of parameters and serious over-fitting. The \(1\times 1\) convolutional layer whose kernel size is \(1\times 1\) is popular for decreasing the channel numbers of the feature maps by offer a channel-wise parametric pooling, often called a feature map pooling or a projection layer. However, the \(1\times 1\) convolutional layer has numerous parameters that need to be learned. Inspired by the \(1\times 1\) convolutional layer, we proposed a channel max pooling, which reduces the feature space by compressing multiple feature maps into one feature map via selecting the maximum values of the same locations from different feature maps. The advantages of the proposed method are twofold as follows: the first is that it decreases the channel numbers of the feature maps whilst retaining their salient features, and the second is that it non-parametric which has no increase in parameters. The experimental results on three image classification datasets show that the proposed method achieves good performance and significantly reduced the parameters of the neural network.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0807205, in part by the National Natural Science Foundation of China (NSFC) No. 61773071, 61922015, in part by the Beijing Nova Program No. Z171100001117049, in part by the Beijing Nova Program Interdisciplinary Cooperation Project No. Z181100006218137, in part by the Fundamental Research Funds for the Central University No. 2018XKJC02, in part by the scholarship from China Scholarship Council (CSC) under Grant CSC No. 201906470049, and in part by the BUPT Excellent Ph.D. Students Foundation No. CX2019109, XTCX201804.

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Lu Cheng, Dongliang Chang, Jiyang Xie & Zhanyu Ma

  2. Institute of Forensic Science, Ministry of Public Security, Beijing, 100876, China

    Rongliang Ma

  3. Forensic Science Institution of Beijing Public Security Bureau, Beijing, 100876, China

    Chunsheng Wu

Authors
  1. Lu Cheng
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  2. Dongliang Chang
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  3. Jiyang Xie
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  4. Rongliang Ma
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  5. Chunsheng Wu
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  6. Zhanyu Ma
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Corresponding author

Correspondence to Dongliang Chang .

Editor information

Editors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Zhen Cui

  2. Nanjing University of Science and Technology, Nanjing, China

    Jinshan Pan

  3. Nanjing University of Science and Technology, Nanjing, China

    Shanshan Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Liang Xiao

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Cheng, L., Chang, D., Xie, J., Ma, R., Wu, C., Ma, Z. (2019). Channel Max Pooling for Image Classification. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_23

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  • DOI: https://doi.org/10.1007/978-3-030-36189-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36188-4

  • Online ISBN: 978-3-030-36189-1

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