Divergence-Agnostic Unsupervised Domain Adaptation by Adversarial Attacks
Conventional machine learning algorithms suffer the problem that the model trained on existing data fails to generalize well to the data sampled from other distributions. To tackle this issue, unsupervised domain adaptation (UDA) transfers the knowledge learned from a well-labeled source domain to a...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 11 vom: 15. Nov., Seite 8196-8211
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| 1. Verfasser: |
Li, Jingjing
(VerfasserIn) |
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| Weitere Verfasser: |
Du, Zhekai,
Zhu, Lei,
Ding, Zhengming,
Lu, Ke,
Shen, Heng Tao |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article
Research Support, Non-U.S. Gov't |
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