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Unsupervised Domain Adaptation

Recent Advances and Future Perspectives
BuchGebunden
223 Seiten
Englisch
Springererschienen am23.04.20242024
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.mehr
Verfügbare Formate
BuchGebunden
EUR181,89
E-BookPDF1 - PDF WatermarkE-Book
EUR171,19

Produkt

KlappentextUnsupervised 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.
Details
ISBN/GTIN978-981-97-1024-9
ProduktartBuch
EinbandartGebunden
Verlag
Erscheinungsjahr2024
Erscheinungsdatum23.04.2024
Auflage2024
Seiten223 Seiten
SpracheEnglisch
Gewicht473 g
IllustrationenXVI, 223 p. 78 illus., 44 illus. in color.
Artikel-Nr.55866951

Inhalt/Kritik

Inhaltsverzeichnis
Chapter 1. Introduction to Domain Adaptation.- Chapter 2. Unsupervised Domain Adaptation Techniques.- Chapter 3. Criterion Optimization-Based Unsupervised Domain.- Chapter 4. Bi-Classifier Adversarial Learning-Based Unsupervised Domain.- Chapter 5. Source-Free Unsupervised Domain Adaptation.- Chapter 6. Active Learning for Unsupervised Domain Adaptation.- Chapter 7. Continual Test-Time Unsupervised Domain Adaptation.- Chapter 8. Applications.- Chapter 9. Research Frontier.mehr

Autor


Jingjing Li is currently a professor with the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). He received his B.Eng., M.Sc. and Ph.D. degrees from UESTC in 2010, 2013, and 2017, respectively. His research interests are in the area of domain adaptation and zero-shot learning. He has co/authored more than 70 peer-reviewed papers, such as IEEE TPAMI, IEEE TIP, IEEE TKDE, CVPR, ICCV, AAAI, IJCAI, and ACM Multimedia. He won Excellent Doctoral Dissertation Award of Chinese Institute of Electronics in 2018.

Lei Zhu is currently a professor with the School of Electronic and Information Engineering, Tongji University. He received his B.Eng. and Ph.D. degrees from Wuhan University of Technology in 2009 and Huazhong University Science and Technology in 2015, respectively. He was a Research Fellow at the University of Queensland (2016-2017). His research interests are in the area of large-scale multimedia contentanalysis and retrieval. Zhu has co/authored more than 100 peer-reviewed papers, such as ACM SIGIR, ACM MM, IEEE TPAMI, IEEE TIP, IEEE TKDE, and ACM TOIS. His publications have attracted more than 5,600 Google citations. At present, he serves as the Associate Editor of IEEE TBD, ACM TOMM, and Information Sciences. He has served as the Area Chair, Senior Program Committee or reviewer for more than 40 well-known international journals and conferences. He won ACM SIGIR 2019 Best Paper Honorable Mention Award, ADMA 2020 Best Paper Award, ChinaMM 2022 Best Student Paper Award, ACM China SIGMM Rising Star Award, Shandong Provincial Entrepreneurship Award for Returned Students, and Shandong Provincial AI Outstanding Youth Award.

Zhekai Du is currently a third-year Ph.D. student with the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). His research interests are domain adaptation, domain generalization, and their applications in computer vision. He received his B.Eng. degree from UESTC in 2018. He has co/authored dozens of papers at the top conferences and journals, like CVPR, ACM Multimedia, ECCV, AAAI, and IEEE TPAMI.