Nan-Fei Jiang, Xu Zhao, Chao-Yang Zhao, Yong-Qi An, Ming Tang, Jin-Qiao Wang. Pruning-aware Sparse Regularization for Network Pruning. Machine Intelligence Research, vol. 20, no. 1, pp.109-120, 2023. https://doi.org/10.1007/s11633-022-1353-0
Citation: Nan-Fei Jiang, Xu Zhao, Chao-Yang Zhao, Yong-Qi An, Ming Tang, Jin-Qiao Wang. Pruning-aware Sparse Regularization for Network Pruning. Machine Intelligence Research, vol. 20, no. 1, pp.109-120, 2023. https://doi.org/10.1007/s11633-022-1353-0

Pruning-aware Sparse Regularization for Network Pruning

doi: 10.1007/s11633-022-1353-0
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  • Author Bio:

    Nan-Fei Jiang received the B. Eng. degree in telecommunications engineering from Qingdao University, China in 2017. He is currently a master student in computer technology at School of Artificial Intelligence, University of Chinese Academy of Sciences, China.His research interests include network pruning, model compression, image and video processing.E-mail: nanfei.jiang@nlpr.ia.ac.cnORCID: 0000-0001-8919-884X

    Xu Zhao received the B. Eng. degree in software engineering from Dalian University of Technology, China in 2014, the Ph. D. degree in pattern recognition and intelligence systems from National Laboratory of Pattern Recognition, Chinese Academy of Sciences, China in 2019. He is currently an assistant professor with NationalLaboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China.His research interests include object detection, scene text detection, image and video processing, and intelligent video surveillance.E-mail: xu.zhao@nlpr.ia.ac.cn (Corresponding author)ORCID: 0000-0001-5888-8872

    Chao-Yang Zhao received the B. Eng. degree in electronic information engineering and M. Sc. degree in circuit and system discipline from University of Electronic Science and Technology of China in 2009 and 2012 respectively, the Ph. D. degree in pattern recognition and intelligence systems from National Laboratory of Pattern Recognition, Chinese Academy of Sciences, China in 2016. He is currently an assistant professor with National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China.His research interests include object detection, image and video processing and intelligent video surveillance.E-mail: chaoyang.zhao@nlpr.ia.ac.cn

    Yong-Qi An received the B. Eng. degree in automation from Beijing Institute of Technology, China in 2021. He is currently a master student in pattern recognition and intelligence systems at School of Artificial Intelligence, University of Chinese Academy of Sciences, China.His research interests include network pruning, knowledge distillation, model compression.E-mail: yongqi.an@nlpr.ia.ac.cn

    Ming Tang received the B. Eng. degree in computer science and engineering, the M. Sc. degree in artificial intelligence from Zhejiang University, China in 1984 and 1987, respectively, and the Ph. D. degree in pattern recognition and intelligent system from Chinese Academy of Sciences, China in 2002. He is currently a professor with National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China.His research interests include computer vision and machine learning.E-mail: tangm@nlpr.ia.ac.cn

    Jin-Qiao Wang received the B. Eng. degree from Hebei University of Technology, China in 2001, and the M. Sc. degree from Tianjin University, China in 2004, the Ph.D. degree in pattern recognition and intelligence systems from the National Laboratory of Pattern Recognition, Chinese Academy of Sciences, China in 2008. He is currently a professor with Chinese Academy of Sciences, China.His research interests include pattern recognition and machine learning, image and video processing, mobile multimedia, and intelligent video surveillance.E-mail: jqwang@nlpr.ia.ac.cn

  • Received Date: 2022-03-30
  • Accepted Date: 2022-06-20
  • Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) by pruning the filters of less importance to the final output accuracy. To reduce the degradation of performance after pruning, many methods utilize the loss with sparse regularization to produce structured sparsity. In this paper, we analyze these sparsity-training-based methods and find that the regularization of unpruned channels is unnecessary. Moreover, it restricts the network′s capacity, which leads to under-fitting. To solve this problem, we propose a novel pruning method, named MaskSparsity, with pruning-aware sparse regularization. MaskSparsity imposes the fine-grained sparse regularization on the specific filters selected by a pruning mask, rather than all the filters of the model. Before the fine-grained sparse regularization of MaskSparity, we can use many methods to get the pruning mask, such as running the global sparse regularization. MaskSparsity achieves a 63.03% float point operations (FLOPs) reduction on ResNet-110 by removing 60.34% of the parameters, with no top-1 accuracy loss on CIFAR-10. On ILSVRC-2012, MaskSparsity reduces more than 51.07% FLOPs on ResNet-50, with only a loss of 0.76% in the top-1 accuracy. The code of this paper is released at https://github.com/CASIA-IVA-Lab/MaskSparsity. We have also integrated the code into a self-developed PyTorch pruning toolkit, named EasyPruner, at https://gitee.com/casia_iva_engineer/easypruner.

     

  • 1 https://github.com/weiaicunzai/pytorch-cifar1002 https://github.com/facebookresearch/pycls
    https://github.com/facebookresearch/pycls
    3 https://github.com/ultralytics/yolov5
    These authors contribute equally to this work
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