Domain‐invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation.
Saved in:
| Title: | Domain‐invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation. |
|---|---|
| Authors: | Wang, Xingmei1 (AUTHOR), Sun, Boxuan1 (AUTHOR) sunboxuan@hrbeu.edu.cn, Dong, Hongbin1 (AUTHOR) |
| Source: | IET Computer Vision (Wiley-Blackwell). Dec2020, Vol. 14 Issue 8, p642-649. 8p. |
| Abstract: | Unsupervised domain adaption aims to reduce the divergence between the source domain and the target domain. The final objective is to learn domain‐invariant features from both domains that get the minimised expected error on the target domain. The divergence between domains which is also called domain shift is mainly between the distributions of domains' samples. Additionally, the label shift is also a tricky challenge in domain adaptation. In this study, domain‐invariant adversarial learning with conditional distribution alignment is proposed to alleviate the effect of domain shift with label shift. To obtain the domain‐invariant features, the proposed method modifies adversarial auto‐encoder architecture and performs semi‐supervised learning to enlarge the inter‐class discrepancy. The marginal distribution is aligned in the adversarial learning process of extracting domain‐invariant features. Meanwhile, the label information is incorporated in this way to align the conditional distribution. The proposed work also theoretically analyses the generalisation bound of the proposed model. Finally, the proposed method is evaluated based on several domain adaptation tasks, including digit classification and object recognition, and achieves state‐of‐the‐art performance. [ABSTRACT FROM AUTHOR] |
| Copyright of IET Computer Vision (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 148144424 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Domain‐invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Xingmei%22">Wang, Xingmei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Boxuan%22">Sun, Boxuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sunboxuan@hrbeu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Dong%2C+Hongbin%22">Dong, Hongbin</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IET+Computer+Vision+%28Wiley-Blackwell%29%22">IET Computer Vision (Wiley-Blackwell)</searchLink>. Dec2020, Vol. 14 Issue 8, p642-649. 8p. – Name: Abstract Label: Abstract Group: Ab Data: Unsupervised domain adaption aims to reduce the divergence between the source domain and the target domain. The final objective is to learn domain‐invariant features from both domains that get the minimised expected error on the target domain. The divergence between domains which is also called domain shift is mainly between the distributions of domains' samples. Additionally, the label shift is also a tricky challenge in domain adaptation. In this study, domain‐invariant adversarial learning with conditional distribution alignment is proposed to alleviate the effect of domain shift with label shift. To obtain the domain‐invariant features, the proposed method modifies adversarial auto‐encoder architecture and performs semi‐supervised learning to enlarge the inter‐class discrepancy. The marginal distribution is aligned in the adversarial learning process of extracting domain‐invariant features. Meanwhile, the label information is incorporated in this way to align the conditional distribution. The proposed work also theoretically analyses the generalisation bound of the proposed model. Finally, the proposed method is evaluated based on several domain adaptation tasks, including digit classification and object recognition, and achieves state‐of‐the‐art performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IET Computer Vision (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=148144424 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1049/iet-cvi.2019.0514 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 642 Titles: – TitleFull: Domain‐invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Xingmei – PersonEntity: Name: NameFull: Sun, Boxuan – PersonEntity: Name: NameFull: Dong, Hongbin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 17519632 Numbering: – Type: volume Value: 14 – Type: issue Value: 8 Titles: – TitleFull: IET Computer Vision (Wiley-Blackwell) Type: main |
| ResultId | 1 |
