Assisted Unsupervised Domain Adaptation

Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Unsupervised domain adaptation (UDA) is a popular machine learning technique that allows one to train models over diverse data collected from different domains. However, this technique requires the learner to collect a large number of properly labeled data samples, which can be costly and unrealistic in many applications. In this work, we propose a decentralized assisted learning framework for UDA. In this framework, a learner has only a limited number of labeled data samples collected from a certain source domain and aims to train a classifier for the target domain. To improve domain adaptation performance, it seeks assistance by interacting with an external service provider, who possesses many labeled data samples collected from a related source domain. We develop an assisted UDA algorithm that avoids data sharing and can significantly improve the learner's domain adaptation performance within a few rounds of interaction. Experiments using deep neural networks on benchmark datasets demonstrate the effectiveness of this algorithm.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Information Theory, ISIT 2023
Number of pages6
ISBN (Electronic)9781665475549
StatePublished - 2023
Event2023 IEEE International Symposium on Information Theory, ISIT 2023 - Taipei, Taiwan, Province of China
Duration: Jun 25 2023Jun 30 2023

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2023 IEEE International Symposium on Information Theory, ISIT 2023
Country/TerritoryTaiwan, Province of China

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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