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Discrimination-Aware Domain Adversarial Neural Network

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Abstract

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.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210046, China

    Yun-Yun Wang, Jian-Min Gu & Chao Wang

  2. Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing, 210046, China

    Yun-Yun Wang, Jian-Min Gu & Chao Wang

  3. College of Computer Science and Technology/College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 210023, China

    Song-Can Chen

  4. Key Laboratory of Pattern Analysis and Machine Intelligence, Ministry of Industry and Information Technology, Nanjing, 210023, China

    Song-Can Chen

  5. School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China

    Hui Xue

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  1. Yun-Yun Wang
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  2. Jian-Min Gu
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  3. Chao Wang
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Correspondence to Yun-Yun Wang.

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Wang, YY., Gu, JM., Wang, C. et al. Discrimination-Aware Domain Adversarial Neural Network. J. Comput. Sci. Technol. 35, 259–267 (2020). https://doi.org/10.1007/s11390-020-9969-4

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  • DOI: https://doi.org/10.1007/s11390-020-9969-4

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