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On the Importance of Attention and Augmentations for Hypothesis Transfer in Domain Adaptation and Generalization
Unsupervised domain adaptation (UDA) aims to mitigate the performance drop due to the distribution shift between the training and testing datasets. UDA methods have achieved performance gains for models trained on a source domain with labeled data to a target domain with only unlabeled data. The sta...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611075/ https://www.ncbi.nlm.nih.gov/pubmed/37896503 http://dx.doi.org/10.3390/s23208409 |