Unsupervised multi-source domain adaptation with no observable source data.
Given trained models from multiple source domains, how can we predict the labels of unlabeled data in a target domain? Unsupervised multi-source domain adaptation (UMDA) aims for predicting the labels of unlabeled target data by transferring the knowledge of multiple source domains. UMDA is a crucia...
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| Auteurs principaux: | , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Public Library of Science (PLoS)
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/23a4fccc91304804b1a1b90f9f7b92e7 |
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