In recent years, Convolutional neural network (CNNs) has gained a significant development in the industrial manufacturing process. However, its application in the environment with high real-time requirements and limited resources is restricted by its huge scale and complicated computing degree. The optimization of the structure of convolutional neural networks has become a research hotspot in the field of deep learning. In this paper, development history, research status and typical methods of structural optimization technology of convolutional neural networks are summarized, which are summarized into five aspects of pruning & sparsification, tensor factorization, knowledge transferring, compacting module designing and automatic design. And a more comprehensive discussion is carried out. Finally, the developments and difficult points of current research in this paper will be analyzed and summarized, and the future development direction and application prospect of network structure optimization field also will be forecast.


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    Title :

    A Review of Structure Optimization of Convolutional Neural Networks


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wang, Yi (editor) / Martinsen, Kristian (editor) / Yu, Tao (editor) / Wang, Kesheng (editor) / Chao, Liu (author) / Chen, Wang (author) / Yu, Li (author) / Changfeng, Xiang (author) / Xiufeng, Zhang (author)

    Conference:

    International Workshop of Advanced Manufacturing and Automation ; 2020 ; Zhanjiang, China October 12, 2020 - October 13, 2020



    Publication date :

    2021-01-23


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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