Iterative Refinement for Multi-Source Visual Domain Adaptation (Extended abstract)

Hanrui Wu, Yuguang Yan, Guosheng Lin, Min Yang, Michael K. Ng, Qingyao Wu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Multi-source domain adaptation (MSDA) aims to leverage the knowledge in multiple source domains to assist the prediction in a target domain, where the source and target domains have different data distributions. This paper presents a MSDA model to investigate both domain discrepancy and domain relevance, whose interactions are also exploited to gradually refine the learning performance. Particularly, the proposed model contains two components, i.e., feature spaces learning and transferred weights learning. The former one minimizes the domain discrepancy and the latter one evaluates the domain relevance. Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
Place of PublicationAnaheim, CA, USA
PublisherIEEE
Pages3829-3830
Number of pages2
ISBN (Electronic)9798350322279
ISBN (Print)9798350322286
DOIs
Publication statusPublished - 3 Apr 2023
Event39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States
Duration: 3 Apr 20237 Apr 2023
https://icde2023.ics.uci.edu/
https://ieeexplore.ieee.org/xpl/conhome/10184508/proceeding

Publication series

NameProceedings - International Conference on Data Engineering
Volume2023-April
ISSN (Print)1063-6382
ISSN (Electronic)2375-026X

Competition

Competition39th IEEE International Conference on Data Engineering, ICDE 2023
Country/TerritoryUnited States
CityAnaheim
Period3/04/237/04/23
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

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