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Deep Domain Generalization

  • Zhengming Ding
  • Handong Zhao
  • Yun Fu
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indiana University-Purdue University IndianapolisIndianapolisUSA
  2. 2.Adobe ResearchSan JoseUSA
  3. 3.Northeastern UniversityBostonUSA

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