Unsupervised Domain Adaptation Techniques

This chapter provides an overview of unsupervised domain adaptation techniques. First, we identify key challenges and limitations in current research, including domain shift, lack of target labels, and negative transfer. We then categorize existing UDA methods based on different technical routes. Theoretical analyses that provide guarantees for successful domain adaptation are also introduced. We summarize the standard datasets and evaluation metrics used for benchmarking different techniques on vision tasks. Finally, we showcase diverse applications and benefits of unsupervised domain adaptation in areas like computer vision, natural language processing, robotics, and healthcare. This chapter equips readers with a solid understanding of the landscape of unsupervised domain adaptation and sets the context for the in-depth technical chapters that follow.