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Deep Domain Generalization

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Learning Representation for Multi-View Data Analysis

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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Abstract

Conventional domain adaptation assumes that target data are still accessible in the training stage. However, we would always confront such cases in reality that the target data are totally blind in the training stage. This is extremely challenging since we have no prior knowledge of the target. Most recently, domain generalization has been well exploited to fight off the challenge through capturing knowledge from multiple source domains and generalizing to the unseen target domains. However, existing domain generalization research efforts all employ shallow structures, so it is difficult for them to well uncover the rich information within the complex data. Therefore, it is easy to ignore the useful knowledge shared by multiple sources and hard to adapt the knowledge to the unseen target domains in the test stage.

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Notes

  1. 1.

    http://www.cs.dartmouth.edu/~chenfang/proj_page/FXR_iccv13/index.php.

  2. 2.

    http://vc.sce.ntu.edu.sg/transfer_learning_domain_adaptation/.

  3. 3.

    http://vasc.ri.cmu.edu/idb/html/face/.

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Authors and Affiliations

  1. Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

    Zhengming Ding

  2. Adobe Research, San Jose, CA, USA

    Handong Zhao

  3. Northeastern University, Boston, MA, USA

    Yun Fu

Authors
  1. Zhengming Ding
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  2. Handong Zhao
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  3. Yun Fu
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Correspondence to Zhengming Ding .

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Ding, Z., Zhao, H., Fu, Y. (2019). Deep Domain Generalization. In: Learning Representation for Multi-View Data Analysis. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-030-00734-8_10

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  • DOI: https://doi.org/10.1007/978-3-030-00734-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00733-1

  • Online ISBN: 978-3-030-00734-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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