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...

Descripción completa

Detalles Bibliográficos
Autores principales: Jeon, Hyunsik, Lee, Seongmin, Kang, U
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270218/
https://www.ncbi.nlm.nih.gov/pubmed/34242258
http://dx.doi.org/10.1371/journal.pone.0253415

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270218/
https://www.ncbi.nlm.nih.gov/pubmed/34242258
http://dx.doi.org/10.1371/journal.pone.0253415

Ejemplares similares