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Multi-representation adaptation network for cross-domain image classification

文献类型：期刊论文

作者Zhu, Yongchun2,3; Zhuang, Fuzhen2,3; Wang, Jindong4; Chen, Jingwu2,3; Shi, Zhiping1; Wu, Wenjuan5; He, Qing2,3
刊名NEURAL NETWORKS
出版日期2019-11-01
卷号119页码:214-221
ISSN号0893-6080
关键词Domain adaptation Multi-representation
DOI10.1016/j.neunet.2019.07.010
英文摘要In image classification, it is often expensive and time-consuming to acquire sufficient labels. To solve this problem, domain adaptation often provides an attractive option given a large amount of labeled data from a similar nature but different domains. Existing approaches mainly align the distributions of representations extracted by a single structure and the representations may only contain partial information, e.g., only contain part of the saturation, brightness, and hue information. Along this line, we propose Multi-Representation Adaptation which can dramatically improve the classification accuracy for cross-domain image classification and specially aims to align the distributions of multiple representations extracted by a hybrid structure named Inception Adaptation Module (IAM). Based on this, we present Multi-Representation Adaptation Network (MRAN) to accomplish the cross-domain image classification task via multi-representation alignment which can capture the information from different aspects. In addition, we extend Maximum Mean Discrepancy (MMD) to compute the adaptation loss. Our approach can be easily implemented by extending most feed-forward models with IAM, and the network can be trained efficiently via back-propagation. Experiments conducted on three benchmark image datasets demonstrate the effectiveness of MRAN. (C) 2019 Elsevier Ltd. All rights reserved.
资助项目National Key Research and Development Program of China[2018YFB1004300] ; National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[U1811461] ; National Natural Science Foundation of China[61773361] ; Project of Youth Innovation Promotion Association CAS[2017146]
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:000488199200017
源URL[http://119.78.100.204/handle/2XEOYT63/4638]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhu, Yongchun; Zhuang, Fuzhen
作者单位1.Capital Normal Univ, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Microsoft Res, Beijing, Peoples R China
5.Renmin Univ China, Sch Informat, Beijing 100872, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Yongchun,Zhuang, Fuzhen,Wang, Jindong,et al. Multi-representation adaptation network for cross-domain image classification[J]. NEURAL NETWORKS,2019,119:214-221.
APA Zhu, Yongchun.,Zhuang, Fuzhen.,Wang, Jindong.,Chen, Jingwu.,Shi, Zhiping.,...&He, Qing.(2019).Multi-representation adaptation network for cross-domain image classification.NEURAL NETWORKS,119,214-221.
MLA Zhu, Yongchun,et al."Multi-representation adaptation network for cross-domain image classification".NEURAL NETWORKS 119(2019):214-221.

入库方式： OAI收割

来源：计算技术研究所

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