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  2020, Vol. 33 Issue (12): 1115-1121    DOI: 10.16451/j.cnki.issn1003-6059.202012006
Structural Learning Representation and Its Applications in Object Detection and Recognition Current Issue| Next Issue| Archive| Adv Search |
Cross-Modal Retrieval via Dual Adversarial Autoencoders
WU Fei1, LUO Xiaokai1, HAN Lu2, ZHENG Xinhao1, XIAO Liang1, SHUAI Zizhen1, JING Xiaoyuan3
1. College of Automation,Nanjing University of Posts and Telecommunications,Nanjing 210003;
2. School of Modern Posts,Nanjing University of Posts and Telecommunications,Nanjing 210003;
3. School of Computer Science,Wuhan University,Wuhan 430072

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Abstract  

How to preserve the original features and reduce the distribution differences of multi-modal data more efficiently during the autoencoder learning process is an important research topic.A cross-modal retrieval approach via dual adversarial autoencoders(DAA) is proposed.The global adversarial network is employed to improve the data reconstruction process of the autoencoders.The min-max is implemented to make it difficult to distinguish the original features and reconstructed features.Consequently,the original features are preserved better.The hidden layer adversarial network generates modality-invariant representations and makes the inter-modal data indistinguishable from each other to reduce the distribution differences of multi-modal data effectively.Experimental results on Wikipedia and NUS-WIDE-10k datasets show the effectiveness of DAA.

Key wordsCross-Modal Retrieval      Adversarial Network      Autoencoder      Modality Difference     
Received: 09 April 2020     
Fund:

National Natural Science Foundation of China(No.61702280),Natural Science Foundation of Jiangsu Province(No.BK20170900)

Corresponding Authors: WU Fei,Ph.D.,lecturer.His research interests include pa-ttern recognition,machine learning and software engineering.   
About author:: LUO Xiaokai,master student.His research interests include pattern recognition and deep learning;HAN Lu,Ph.D.,lecturer.Her research interests include pattern recognition and machine learning;ZHENG Xinhao,master student.His research interests include pattern recognition and deep learning;SHUAI Zizhen,master student.Her research interests include pattern recognition and deep learning;JING Xiaoyuan,Ph.D.,professor.His research interests include pattern recognition,image processing and machine learning.
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WU Fei
LUO Xiaokai
HAN Lu
ZHENG Xinhao
XIAO Liang
SHUAI Zizhen
JING Xiaoyuan
Cite this article:   
WU Fei,LUO Xiaokai,HAN Lu等. Cross-Modal Retrieval via Dual Adversarial Autoencoders[J]. , 2020, 33(12): 1115-1121.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202012006      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I12/1115
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