GLC++ : Source-Free Universal Domain Adaptation Through Global-Local Clustering and Contrastive Affinity Learning
Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain...
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| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 47(2025), 11 vom: 05. Okt., Seite 10646-10663
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| Auteur principal: |
Qu, Sanqing
(Auteur) |
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| Autres auteurs: |
Zou, Tianpei,
Rohrbein, Florian,
Lu, Cewu,
Chen, Guang,
Tao, Dacheng,
Jiang, Changjun |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2025
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
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| Sujets: | Journal Article |
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