Abstract：The traditional deep neural network cannot be deployed on the edge devices with limited computing capacity due to numerous parameters and high computation. In this paper, a lightweight network based on neural architecture search is specially designed to solve this problem. Convolution units of different groups are regarded as search space, and neural architecture search is utilized to obtain both the group structure and the overall architecture of the network. In the meanwhile, a cycle annealing search strategy is put forward to solve the multi-objective optimization problem of neural architecture search with the consideration of the accuracy and the computation cost of the model. Experiments on datasets show that the proposed network model achieves a better performance than the state-of-the-art methods.
姚潇, 史叶伟, 霍冠英, 徐宁. 基于神经网络结构搜索的轻量化网络构建[J]. 模式识别与人工智能, 2021, 34(11): 1038-1048.
YAO Xiao, SHI Yewei, HUO Guanying, XU Ning. Lightweight Model Construction Based on Neural Architecture Search. , 2021, 34(11): 1038-1048.
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