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| A Review on Architecture Adaptation of Neural Networks |
| LI Shu1, QIN Xianping1, ZHAI Xiaotong1, ZHANG Long1, ZHONG Guoqiang1, XIANG Shiming2,3 |
1. College of Computer Science and Technology, Ocean University of China, Qingdao 266404;
2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190;
3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049 |
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Abstract Network architecture adaptation aims to automatically design and optimize the neural network architectures based on specific learning tasks and data to meet the comprehensive needs of intelligent perception learning tasks in open environment. This paper is intended to provide a comprehensive review of network architecture adaptation methods. Firstly, the main methods of neural architecture search are elucidated and analyzed. Then, the research progress of network architecture adaptation is presented from three aspects: lightweight neural architecture search, intelligent perception tasks and continuous learning. On this basis, an adaptive evaluation index system of deep neural network components and architectures for open environment applications is established, and a network architecture adaptive method is proposed. Through the attention-guided micro-architecture adaptive mechanism and progressive discretization strategy, adaptive adjustment, optimization and gradual discretization of network structures are realized in the optimization process. The proposed method is compared with the existing methods. Finally, problems and challenges of current methods are discussed, and the future research directions are prospected.
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Received: 10 October 2023
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| Fund:Supported by National Key Research and Development Program of China(No.2018AAA0100400), Natural Science Foundation of Shandong Province(No.ZR2020MF131,ZR2021ZD19), Science and Technology Program of Qingdao(No.21-1-4-ny-19-nsh) |
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Corresponding Authors:
ZHONG Guoqiang, Ph.D., professor. His research inte-rests include artificial intelligence and deep learning.
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About author:: LI Shu, master student. Her research interests include deep learning and neural architecture search.
QIN Xianping, master student. Her research interests include deep learning and neural architecture search.
ZHAI Xiaotong, master student. Her research interests include deep learning and neural architecture search.
ZHANG Long, master student. His research interests include marine chemistry and neural architecture search.
XIANG Shiming, Ph.D., professor. His research interests include pattern recognition, machine learning and computer vision. |
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2023, Vol. 36 
