Pruning-aware Sparse Regularization for Network Pruning

doi:10.1007/s11633-022-1353-0

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Pruning-aware Sparse Regularization for Network Pruning
	Nan-Fei Jiang 1,2
发表期刊	Machine Intelligence Research
ISSN	2731-538X
	2023
卷号	20 期号:1 页码:109-120
摘要	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.
关键词	Deep learning convolutional neural network (CNN) model compression and acceleration network pruning regularization
DOI	10.1007/s11633-022-1353-0
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50903
专题	学术期刊_Machine Intelligence Research
作者单位	1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Nan-Fei Jiang. Pruning-aware Sparse Regularization for Network Pruning[J]. Machine Intelligence Research,2023,20(1):109-120.
APA	Nan-Fei Jiang.(2023).Pruning-aware Sparse Regularization for Network Pruning.Machine Intelligence Research,20(1),109-120.
MLA	Nan-Fei Jiang."Pruning-aware Sparse Regularization for Network Pruning".Machine Intelligence Research 20.1(2023):109-120.

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