Unsupervised Domain Adaptation (Record no. 87882)
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| fixed length control field | 04628nam a22005895i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-981-97-1025-6 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730171918.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240422s2024 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789819710256 |
| -- | 978-981-97-1025-6 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.31 |
| 100 1# - AUTHOR NAME | |
| Author | Li, Jingjing. |
| 245 10 - TITLE STATEMENT | |
| Title | Unsupervised Domain Adaptation |
| Sub Title | Recent Advances and Future Perspectives / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XVI, 223 p. 78 illus., 44 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Machine Learning: Foundations, Methodologies, and Applications, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. The book begins with a clear introduction to the UDA problem and is mainly organized into four technical sections, each focused on a specific piece of UDA research. The first section covers criterion optimization-based UDA, which aims to learn domain-invariant representations by minimizing the discrepancy between source and target domains. The second section discusses bi-classifier adversarial learning-based UDA, which creatively leverages adversarial learning by conducting a minimax game between the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents active learning for UDA, which combines domain adaptation and active learning to reduce the amount of labeled data needed for adaptation. This book is suitable for researchers, graduate students, and practitioners who are interested in UDA and its applications in various fields, primarily in computer vision. The chapters are authored by leading experts in the field and provide a comprehensive and in-depth analysis of the current UDA methods and new directions for future research. With its broad coverage and cutting-edge research, this book is a valuable resource for anyone looking to advance their knowledge of UDA. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Data processing. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Zhu, Lei. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Du, Zhekai. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-981-97-1025-6 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Singapore : |
| -- | Springer Nature Singapore : |
| -- | Imprint: Springer, |
| -- | 2024. |
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| -- | text |
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| -- | computer |
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| -- | rdamedia |
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| -- | online resource |
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| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Distribution (Probability theory). |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Distribution Theory. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2730-9916 |
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