Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (2): 259-267.doi: 10.1007/s11390-020-9969-4

• Special Section on Learning and Mining in Dynamic Environments • Previous Articles     Next Articles

Discrimination-Aware Domain Adversarial Neural Network

Yun-Yun Wang1,2, Member, CCF, Jian-Min Gu1,2, Chao Wang1,2, Song-Can Chen3,4, Member, CCF, Hui Xue5, Member, CCF        

  1. 1 College of Computer Science and Engineering, Nanjing University of Posts and Telecommunications Nanjing 210046, China;
    2 Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing 210046, China;
    3 College of Computer Science and Technology/College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 210023, China;
    4 Key Laboratory of Pattern Analysis and Machine Intelligence, Ministry of Industry and Information Technology Nanjing 210023, China;
    5 School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
  • Received:2019-08-20 Revised:2020-01-02 Online:2020-03-05 Published:2020-03-18
  • About author:Yun-Yun Wang received her Ph.D. degree in computer science and technology from Nanjing University of Aeronautics and Astronautics, Nanjing, in 2012. She is currently with the College of Computer Science and Technology in Nanjing University of Posts and Telecommunications. Her current research interests include pattern recognition, machine learning and neural computing.
  • Supported by:
    The work was supported by the National Natural Science Foundation of China under Grant Nos. 61876091 and 61772284, the China Postdoctoral Science Foundation under Grant No. 2019M651918, and the Open Foundation of Key Laboratory of Pattern Analysis and Machine Intelligence of Ministry of Industry and Information Technology of China.

The domain adversarial neural network (DANN) methods have been successfully proposed and attracted much attention recently. In DANNs, a discriminator is trained to discriminate the domain labels of features generated by a generator, whereas the generator attempts to confuse it such that the distributions between domains are aligned. As a result, it actually encourages the whole alignment or transfer between domains, while the inter-class discriminative information across domains is not considered. In this paper, we present a Discrimination-Aware Domain Adversarial Neural Network (DA2NN) method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain adaptation. DA2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple discriminators. Empirical results show that DA2NN can achieve better classification performance compared with the DANN methods.

Key words: adversarial learning; inter-class separation; deep neural network; discrimination-aware; domain adaptation;

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