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 language | English |
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Article number | 8861136 |
Pages (from-to) | 766-785 |
Number of pages | 20 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 43 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- covariate shift
- domain adaptation
- Machine learning
- pattern recognition
- sample selection bias
- transfer learning