MeTa Learning-Based Optimization of Unsupervised Domain Adaptation Deep Networks | |
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學年 | 113 |
學期 | 1 |
出版(發表)日期 | 2025-01-10 |
作品名稱 | MeTa Learning-Based Optimization of Unsupervised Domain Adaptation Deep Networks |
作品名稱(其他語言) | |
著者 | Hsiau-Wen Lin, Trang-Thi Ho, Ching-Ting Tu, Hwei Jen Lin, Chen-Hsiang Yu |
單位 | |
出版者 | |
著錄名稱、卷期、頁數 | Mathematics, 13(2):226 |
摘要 | This paper introduces a novel unsupervised domain adaptation (UDA) method, MeTa Discriminative Class-Wise MMD (MCWMMD), which combines meta-learning with a Class-Wise Maximum Mean Discrepancy (MMD) approach to enhance domain adaptation. Traditional MMD methods align overall distributions but struggle with classwise alignment, reducing feature distinguishability. MCWMMD incorporates a metamodule to dynamically learn a deep kernel for MMD, improving alignment accuracy and model adaptability. This meta-learning technique enhances the model’s ability to generalize across tasks by ensuring domain-invariant and class-discriminative feature representations. Despite the complexity of the method, including the need for meta-module training, it presents a significant advancement in UDA. Future work will explore scalability in diverse real-world scenarios and further optimize the meta-learning framework. MCWMMD offers a promising solution to the persistent challenge of domain adaptation, paving the way for more adaptable and generalizable deep learning models. |
關鍵字 | unsupervised domain adaptation; maximum mean discrepancy (MMD); discriminative class-wise MMD (DCWMMD); meta-learning; deep kernel; feature distributions; domain shift; transfer learning |
語言 | en_US |
ISSN | 2227-7390 |
期刊性質 | 國內 |
收錄於 | SCI EI |
產學合作 | |
通訊作者 | |
審稿制度 | 是 |
國別 | CHE |
公開徵稿 | |
出版型式 | ,電子版 |
相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126771 ) |