ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (1): 22-33.doi: 10.7544/issn1000-1239.2021.20190851

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Deep Neural Architecture Search: A Survey

Meng Ziyao1,2, Gu Xue1,2, Liang Yanchun1,2, Xu Dong3, Wu Chunguo1   

  1. 1(Key Laboratory of Symbol Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun 130012);2(Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering (Zhuhai College of Jilin University), Ministry of Education, Zhuhai, Guangdong 519041);3(Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri, USA MO65211)
  • Online:2021-01-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61972174, 61876069, 61876207), the Key Research and Development Project of Jilin Province (20180201045GX, 20180201067GX), the Natural Science Foundation of Jilin Province(20200201163JC), the Science and Technology Planning Project of Guangdong Province (2020A0505100018), the Guangdong Key-Project for Applied Fundamental Research (2018KZDXM076), and the Guangdong Premier Key-Discipline Enhancement Scheme (2016GDYSZDXK036).

Abstract: Deep learning has achieved excellent results on data tasks with multiple modalities such as images, speech, and text. However, designing networks manually for specific tasks is time-consuming and requires a certain level of expertise and design experience from the designer. In the face of today’s increasingly complex network architectures, relying on manual design alone increasingly becomes complex. For this reason, automatic architecture search of neural networks with the help of algorithms has become a hot research topic. The approach of neural architecture search involves three aspects: search space, search strategy, and performance evaluation strategy. The search strategy samples a network architecture in the search space, evaluates the network architecture by a performance evaluation strategy, and feed-back the results to the search strategy to guide it to select a better network architecture, and obtains the optimal network architecture through continuous iterations. In order to better sort out the methods of neural architecture search, we summarize the common methods in recent years from search space, search strategy and performance evaluation strategy, and analyze their strengths and weaknesses.

Key words: deep learning, neural architecture search (NAS), search space, search strategy, perfor-mance evaluation

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