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Overview: Unsupervised domain adaptation (UDA) is a challenging problem in Machine Learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. In recent years, UDA has received significant attention from the research community due to its applicability in various real-world scenarios. This book provides a comprehensive review of state-of-the-art UDA methods and explores new variants of UDA that have the potential to advance the field. 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. This book summarizes their latest advances in unsupervised domain adaptation. The extensive technical coverage offers both consolidating perspectives on fundamental theory as well as exposure to latest advancements driving progress in the field. It is our hope that this book can serve as an accessible guide for new researchers as well as an insightful reference for experienced academics pursuing advancements in this rapidly evolving field. The passion and insights gathered here may seed innovative ideas that lead to breakthroughs in unsupervised domain adaptation.
Genre: Non-Fiction > Tech & Devices
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