The exponential growth of digital data requires the development of efficient machine learning models. Nonetheless, it is ubiquitous that data in different scenarios (domains) conform to different underlying distributions. A machine learning model that has been trained in a specific domain may not work well when presented with data from other domains. Domain adaptation arises when we aim to exploit heterogeneous information from different but related domains to transfer knowledge to the target domain of interest. With the aid of domain adaptation technology, one can build more effective machine learning models that can perform well in a wide range of real-world scenarios.
This tutorial illustrates the techniques and strategies used in domain adaptation, allowing one to build more accurate and reliable machine learning models that can adapt to different types of data. With clear explanations and experimental examples, this tutorial is essential for anyone who hopes to deepen their understanding of this important subarea in transfer learning.
Domain adaptation is a crucial technique in the field of machine learning that allows models to adapt to different types of data. With the increasing availability of data from diverse sources, there is a growing demand for domain adaptation techniques in a wide range of industries and applications, making it a valuable skill set for researchers, students, and industry professionals, etc. This tutorial systemically describes the key contents and covers the recent advancements of domain adaptation, including conventional unsupervised domain adaptation and other promising variants. It would be valuable for researchers, students, and industry professionals to keep up-to-date on the latest advancements in this important area of machine learning.
Jingjing Li received the M.Sc. and Ph.D. degrees in computer science from the University of Electronic Science and Technology of China in 2013 and 2017, respectively. He is currently a full Professor with the School of Computer Science and Engineering, University of Electronic Science and Technology of China. His current research interests include computer vision, machine learning and multimedia analysis, especially transfer learning, domain adaptation and zero-shot learning. Dr. Jingjing Li has published over 70 peer-reviewed papers on top-ranking journals and conferences, including IEEE TPAMI, TIP, TKDE, CVPR and NeurIPS. He has long served as a reviewer/PC/SPC/AC for TPAMI, TIP, TOIS, AAAI, CVPR, WACV and ACM MM. He won the Excellent Doctoral Thesis Award of The Chinese Institute of Electronics, and The Excellent Young Scholar of Wu Wen Jun AI Science & Technology Award.