Synthetic Source Universal Domain Adaptation through Contrastive Learning

Universal domain adaptation (UDA) is a crucial research topic for efficient deep learning model training using data from various imaging sensors. However, its development is affected by unlabeled target data. Moreover, the nonexistence of prior knowledge of the source and target domain makes it more...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autor principal: Jungchan Cho
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
Acceso en línea:https://doaj.org/article/7a576453521840dfb764b12bf8684110
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares