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International Conference on Web Information Systems Engineering

WISE 2018: Web Information Systems Engineering – WISE 2018 pp 253–268Cite as

Improving Maximum Classifier Discrepancy by Considering Joint Distribution for Domain Adaptation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11234))

Abstract

Recently, domain adaptation has gained great popularity, while most researchers are focusing on domains in homogenous modalities, e.g., image domains. In reality, heterogeneous domains are pretty common and more challenging. In this paper, we present MCD-JD—a Maximum Classifier Discrepancy model which considers the joint distribution of the source and target domain data for heterogeneous domain adaption. MCD-JD derives from Generative Adversarial Networks (GAN) consisting of two parts, i.e., minimizing the discrepancy of joint distribution, and maximizing classifier discrepancy. Specifically, the first part uses the Maximum Mean Discrepancy (MMD) regularization to adapt the data distributions between source and target domains. The second part utilizes two different classifiers to maximize their discrepancy of making predictions on the target domain data, which further minimizes the discrepancy of data distributions between source and target domains. We collect a dataset depicting real-world events (e.g., protests, explosions, etc.) from multiple heterogeneous data domains, including news media textual articles, social media (Flickr) images, and YouTube videos. Extensive experiments conducted on the real-world dataset manifest the effectiveness of MCD-JD, which outperforms state-of-the-art benchmark models.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61703109, No. 91748107, No.U1611461), the Guangdong Innovative Research Team Program (No. 2014ZT05G157), Science and Technology Program of Guangdong Province, China (No. 2016A010101012), and CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, 100190, Beijing, China. (No. CASNDST201703), and an internal grant from City University of Hong Kong (project no. 9610367).

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Zehang Lin, Zhenguo Yang, Runwei Situ, Feitao Huang & Wenyin Liu

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Jianming Lv

  3. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Qing Li

Authors
  1. Zehang Lin
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  2. Zhenguo Yang
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  3. Runwei Situ
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  4. Feitao Huang
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  5. Jianming Lv
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  6. Qing Li
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  7. Wenyin Liu
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Corresponding authors

Correspondence to Zhenguo Yang or Wenyin Liu .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. University of Victoria, Footscray, VIC, Australia

    Hua Wang

  4. University of New South Wales, Sydney, NSW, Australia

    Hye-Young Paik

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

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Lin, Z. et al. (2018). Improving Maximum Classifier Discrepancy by Considering Joint Distribution for Domain Adaptation. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_18

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  • DOI: https://doi.org/10.1007/978-3-030-02925-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02924-1

  • Online ISBN: 978-3-030-02925-8

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