Visual domain adaptation in the deep learning era
Auteur :
Csurka, Gabriela / Hospedales, Timothy M. / Salzmann, Mathieu / Tommasi, Tatiana
Éditeur :
Springer International Publishing AG
ISBN :
9783031791703
Date de publication :
5 avr. 2022
Dimensions :
23,5 x 19,1 cm
Langue :
Anglais
Pays d'origine :
Suisse
Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains.