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Visualize and Compress Single Logo Recognition Neural Network

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Book cover Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 951))

Abstract

Logo recognition by Convolutional Neural Networks (CNNs) on a smartphone requires the network to be both accurate and small. In our previous work [1], we proposed the accompanying dataset method for single logo recognition to increase the recall and precision of the target logo recognition. However, the reason why it works was unclear, thus it was hard to compress the network while maintaining the same accuracy. In this paper, we use DeconvNet [9] to visualize our network’s feature maps and propose a metric to analyze them quantitatively. Finally, we obtain a better understanding of the influences in the network brought by accompanying datasets. Under its guidance, an effective way to compress the network is devised by us. The experiments show that we can reduce the size of the neural network’s first layer by 30% while only lower the recall and precision by 0.014 and 0.01. The training time is also saved by 40% due to the network compression.

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Acknowledgments

The work was supported in part by the National High-tech R&D Program of China (863 Program) (2015AA017201) and National Key Research and Development Program of China (2016QY01W0200). The authors are very grateful to the anonymous viewers of this paper.

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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

    Yulong Wang & Haoxin Zhang

Authors
  1. Yulong Wang
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  2. Haoxin Zhang
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Corresponding author

Correspondence to Yulong Wang .

Editor information

Editors and Affiliations

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

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Wang, Y., Zhang, H. (2018). Visualize and Compress Single Logo Recognition Neural Network. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_29

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  • DOI: https://doi.org/10.1007/978-981-13-2826-8_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2825-1

  • Online ISBN: 978-981-13-2826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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