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Titlebook: Unsupervised Domain Adaptation; Recent Advances and Jingjing Li,Lei Zhu,Zhekai Du Book 2024 The Editor(s) (if applicable) and The Author(s

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發(fā)表于 2025-3-21 16:17:34 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Unsupervised Domain Adaptation
副標(biāo)題Recent Advances and
編輯Jingjing Li,Lei Zhu,Zhekai Du
視頻videohttp://file.papertrans.cn/943/942522/942522.mp4
概述Covers not only conventional domain adaptation, but also source-free domain adaptation and active domain adaptation.Presents unique methods to approach domain adaptation from novel perspectives, which
叢書(shū)名稱(chēng)Machine Learning: Foundations, Methodologies, and Applications
圖書(shū)封面Titlebook: Unsupervised Domain Adaptation; Recent Advances and  Jingjing Li,Lei Zhu,Zhekai Du Book 2024 The Editor(s) (if applicable) and The Author(s
描述.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 act
出版日期Book 2024
關(guān)鍵詞Transfer Learning; Adversarial Learning; Source-Free Domain adaptation; Active Domain Adaptation; Unsupe
版次1
doihttps://doi.org/10.1007/978-981-97-1025-6
isbn_softcover978-981-97-1027-0
isbn_ebook978-981-97-1025-6Series ISSN 2730-9908 Series E-ISSN 2730-9916
issn_series 2730-9908
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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書(shū)目名稱(chēng)Unsupervised Domain Adaptation影響因子(影響力)




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書(shū)目名稱(chēng)Unsupervised Domain Adaptation網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Unsupervised Domain Adaptation網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Unsupervised Domain Adaptation被引頻次




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沙發(fā)
發(fā)表于 2025-3-21 21:24:07 | 只看該作者
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發(fā)表于 2025-3-22 03:13:04 | 只看該作者
Machine Learning: Foundations, Methodologies, and Applicationshttp://image.papertrans.cn/u/image/942522.jpg
地板
發(fā)表于 2025-3-22 06:02:16 | 只看該作者
https://doi.org/10.1007/978-981-97-1025-6Transfer Learning; Adversarial Learning; Source-Free Domain adaptation; Active Domain Adaptation; Unsupe
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發(fā)表于 2025-3-22 11:58:33 | 只看該作者
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發(fā)表于 2025-3-22 13:48:05 | 只看該作者
Jingjing Li,Lei Zhu,Zhekai DuCovers not only conventional domain adaptation, but also source-free domain adaptation and active domain adaptation.Presents unique methods to approach domain adaptation from novel perspectives, which
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2730-9908 tween 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 act978-981-97-1027-0978-981-97-1025-6Series ISSN 2730-9908 Series E-ISSN 2730-9916
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發(fā)表于 2025-3-23 02:53:32 | 只看該作者
Bi-Classifier Adversarial Learning-Based Unsupervised Domain Adaptation,er focuses on preserving target decision boundaries. Experiments on several domain adaptation benchmarks demonstrate the efficacy of both CGDM and uneven bi-classifier learning in boosting adaptation performance.
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發(fā)表于 2025-3-23 07:50:25 | 只看該作者
Source-Free Unsupervised Domain Adaptation,ameter sharing further reduces the number of learnable parameters for efficient adaptation. Model perturbation avoids distorting weights like fine-tuning and is more flexible than only updating batch normalization statistics. Experiments demonstrate the effectiveness of both data and model perturbat
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