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024 7 _a10.1007/978-981-97-1025-6
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100 1 _aLi, Jingjing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100865
245 1 0 _aUnsupervised Domain Adaptation
_h[electronic resource] :
_bRecent Advances and Future Perspectives /
_cby Jingjing Li, Lei Zhu, Zhekai Du.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXVI, 223 p. 78 illus., 44 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
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_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
505 0 _aChapter 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 _aUnsupervised 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 _aMachine learning.
_91831
650 0 _aArtificial intelligence
_xData processing.
_921787
650 0 _aData mining.
_93907
650 0 _aDistribution (Probability theory).
_910767
650 1 4 _aMachine Learning.
_91831
650 2 4 _aData Science.
_934092
650 2 4 _aData Mining and Knowledge Discovery.
_9100868
650 2 4 _aDistribution Theory.
_979417
700 1 _aZhu, Lei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100869
700 1 _aDu, Zhekai.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100871
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789819710270
830 0 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
_9100874
856 4 0 _uhttps://doi.org/10.1007/978-981-97-1025-6
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