Menthol
发表于 2025-3-21 16:17:34
书目名称Unsupervised Domain Adaptation影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0942522<br><br> <br><br>书目名称Unsupervised Domain Adaptation影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0942522<br><br> <br><br>书目名称Unsupervised Domain Adaptation网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0942522<br><br> <br><br>书目名称Unsupervised Domain Adaptation网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0942522<br><br> <br><br>书目名称Unsupervised Domain Adaptation被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0942522<br><br> <br><br>书目名称Unsupervised Domain Adaptation被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0942522<br><br> <br><br>书目名称Unsupervised Domain Adaptation年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0942522<br><br> <br><br>书目名称Unsupervised Domain Adaptation年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0942522<br><br> <br><br>书目名称Unsupervised Domain Adaptation读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0942522<br><br> <br><br>书目名称Unsupervised Domain Adaptation读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0942522<br><br> <br><br>
anaphylaxis
发表于 2025-3-21 21:24:07
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Immortal
发表于 2025-3-22 03:13:04
Machine Learning: Foundations, Methodologies, and Applicationshttp://image.papertrans.cn/u/image/942522.jpg
Intercept
发表于 2025-3-22 06:02:16
https://doi.org/10.1007/978-981-97-1025-6Transfer Learning; Adversarial Learning; Source-Free Domain adaptation; Active Domain Adaptation; Unsupe
蜡烛
发表于 2025-3-22 11:58:33
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前奏曲
发表于 2025-3-22 13:48:05
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
使隔离
发表于 2025-3-22 18:49:20
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攀登
发表于 2025-3-22 22:25:16
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
警告
发表于 2025-3-23 02:53:32
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.
HIKE
发表于 2025-3-23 07:50:25
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