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| Network Pruning via Automatic Mending Strategy |
| SU Qihang1,2, QIAN Yeqiang3, YUAN Wei3, YANG Ming1,2, WANG Chunxiang1,2 |
1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240;
2. Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240;
3. UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai 200240 |
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Abstract To alleviate the problem of the application of deep neural network being restricted owing to the massive computation resources, many network compression strategies including network pruning are put forward. Most of the network pruning methods based on greedy algorithm include training, pruning and fine-tuning, and therefore the optimal pruned structure cannot be obtained. In this paper, combining the rule-based method and the automatic search method, a network pruning method via automatic mending strategy is proposed. The whole pruning process is comprised of four stages: training, pre-pruning, mending and fine-tuning. The structure of the pre-pruned model is improved in the additional mending stage. Particularly, the neural architecture search is utilized to implement network mending. The search space and an efficient search strategy are designed. The estimation process is accelerated based on the filter ranking of the pre-pruning stage. Experiments show that the proposed method can guarantee the network accuracy in the case of high pruning rate.
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Received: 28 June 2021
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| Fund:National Natural Science Foundation of China(No.U1764264,62103261) |
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Corresponding Authors:
WANG Chunxiang, Ph.D., associate professor. Her research interests mobile robots, autonomous driving and assistant driving.
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About author:: SU Qihang, master student. His research interests include computer vision and network compression.
QIAN Yeqiang, Ph.D. His research inte-rests include computer vision, pattern recognition, machine learning, and their applications in intelligent transportation systems.
YUAN Wei, Ph.D. His research interests include autonomous driving system, computer vision, deep learning and vehicle control.
YANG Ming, Ph.D., professor. His research interests include positioning and navigation in the low speed intelligent vehicles. |
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2022, Vol. 35 
