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Regularized Deep Convolutional Neural Networks for Feature Extraction and Classification

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10635))

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Abstract

Deep Convolutional Neural Networks (DCNNs) are the state-of-the-art in fields such as visual object recognition, handwriting and speech recognition. The DCNNs include a large number of layers, a huge number of units, and connections. Therefore, with the huge number of parameters, overfitting can occur. In order to prevent the network against this problem, regularization techniques have been applied in different positions. In this paper, we show that with the right combination of applied regularization techniques such as fully connected dropout, max pooling dropout, L2 regularization and He initialization, it is possible to achieve good results in object recognition with small networks and without data augmentation.

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Authors and Affiliations

  1. LATIS Research Lab, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia

    Khaoula Jayech

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  1. Khaoula Jayech
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Correspondence to Khaoula Jayech .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Jayech, K. (2017). Regularized Deep Convolutional Neural Networks for Feature Extraction and Classification. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_45

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  • DOI: https://doi.org/10.1007/978-3-319-70096-0_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70095-3

  • Online ISBN: 978-3-319-70096-0

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