學年
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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)
|
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著者
|
Hsiau-Wen Lin, Trang-Thi Ho, Ching-Ting Tu, Hwei Jen Lin, Chen-Hsiang Yu |
單位
|
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出版者
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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
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產學合作
|
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通訊作者
|
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審稿制度
|
是 |
國別
|
CHE |
公開徵稿
|
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出版型式
|
,電子版 |