A review of domain adaptation without target labels
It provides a structured overview for researchers working on domain adaptation, but is incremental as it reviews existing methods without introducing new techniques.
This review categorizes domain adaptation methods into sample-based, feature-based, and inference-based approaches to address how classifiers can generalize from a source to a target domain without target labels, highlighting recurring ideas and open questions for future research.
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.