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Transfer channel pruning for compressing deep domain adaptation models
Yu, Chaohui1,2; Wang, Jindong3; Chen, Yiqiang1,2; Qin, Xin1,2
2019-11-01
发表期刊INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
ISSN1868-8071
卷号10期号:11页码:3129-3144
摘要Deep unsupervised domain adaptation has recently received increasing attention from researchers. However, existing methods are computationally intensive due to the computational cost of convolutional neural networks (CNN) adopted by most work. There is no effective network compression method for such problem. In this paper, we propose a unified transfer channel pruning (TCP) method for accelerating deep unsupervised domain adaptation (UDA) models. TCP method is capable of compressing the deep UDA model by pruning less important channels while simultaneously learning transferable features by reducing the cross-domain distribution divergence. Therefore, it reduces the impact of negative transfer and maintains competitive performance on the target task. To the best of our knowledge, TCP method is the first approach that aims at accelerating deep unsupervised domain adaptation models. TCP method is validated on two main kinds of UDA methods: the discrepancy-based methods and the adversarial-based methods. In addition, it is validated on two benchmark datasets: Office-31 and ImageCLEF-DA with two common backbone networks - VGG16 and ResNet50. Experimental results demonstrate that our TCP method achieves comparable or better classification accuracy than other comparison methods while significantly reducing the computational cost. To be more specific, in VGG16, we get even higher accuracy after pruning 26% floating point operations (FLOPs); in ResNet50, we also get higher accuracy on half of the tasks after pruning 12% FLOPs for both discrepancy-based methods and adversarial-based methods.
关键词Unsupervised domain adaptation Transfer channel pruning Accelerating
DOI10.1007/s13042-019-01004-6
收录类别SCI
语种英语
资助项目National Key Research & Development Plan of China[2017YFB1002802] ; NSFC[61572471] ; Beijing Municipal Science & Technology Commission[Z171100000117017]
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000494802500010
出版者SPRINGER HEIDELBERG
引用统计
被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/14839
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Yiqiang
作者单位1.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Microsoft Res Asia, Beijing, Peoples R China
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GB/T 7714
Yu, Chaohui,Wang, Jindong,Chen, Yiqiang,et al. Transfer channel pruning for compressing deep domain adaptation models[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2019,10(11):3129-3144.
APA Yu, Chaohui,Wang, Jindong,Chen, Yiqiang,&Qin, Xin.(2019).Transfer channel pruning for compressing deep domain adaptation models.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,10(11),3129-3144.
MLA Yu, Chaohui,et al."Transfer channel pruning for compressing deep domain adaptation models".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 10.11(2019):3129-3144.
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