A Review of Domain Adaptation without Target Labels

Wouter M. Kouw, Marco Loog

Research output: Contribution to journalReview articlepeer-review

12 Citations (Scopus)

Abstract

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: How can a classifier learn from a source domain and generalize to a target domain We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based, and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting, and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.

Original languageEnglish
Article number8861136
Pages (from-to)766-785
Number of pages20
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume43
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • covariate shift
  • domain adaptation
  • Machine learning
  • pattern recognition
  • sample selection bias
  • transfer learning

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