Introduction to Domain Adaptation

Domain adaptation refers to the machine learning techniques that enable models trained on data from a source domain to perform well on a different but related target domain. This chapter provides the necessary background on transfer learning and its relationship to domain adaptation. We define the domain adaptation problem and discuss the categorization of domain adaptation techniques into supervised, semi-supervised, and unsupervised paradigms. The rest of the book will focus specifically on unsupervised domain adaptation. We motivate the need for unsupervised domain adaptation, discuss its advantages over other paradigms, and provide a high-level overview of the common approaches and techniques. The chapter concludes by outlining the structure and main contents of the book. Through this chapter, readers will gain the prerequisite knowledge to understand the more in-depth treatment of unsupervised domain adaptation techniques in the following chapters.