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Generative models for noise-robust training in unsupervised domain adaptation.

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Title: Generative models for noise-robust training in unsupervised domain adaptation.
Authors: Deng, Zhongying1, zd294@cam.ac.uk, Li, Da2, He, Junjun3, Peng, Xiaojiang1,4, pengxiaojiang@sztu.edu.cn, Song, Yi-Zhe5, Xiang, Tao5
Source: Pattern Recognition; Apr2026:Part B, Vol. 172, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 189885434
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Generative models for noise-robust training in unsupervised domain adaptation.
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  Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>; Apr2026:Part B, Vol. 172, pN.PAG-N.PAG, 1p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=189885434
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      – Type: doi
        Value: 10.1016/j.patcog.2025.112450
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      – Code: eng
        Text: English
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      – TitleFull: Generative models for noise-robust training in unsupervised domain adaptation.
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              Text: Apr2026:Part B
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              Y: 2026
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