Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Chuangji Meng"'
Publikováno v:
Mathematics, Vol 10, Iss 14, p 2531 (2022)
Domain discrepancy is a key research problem in the field of deep domain adaptation. Two main strategies are used to reduce the discrepancy: the parametric method and the nonparametric method. Both methods have achieved good results in practical appl
Externí odkaz:
https://doaj.org/article/1968b6f496b247848522888b65501509
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3dec8c3cb9fb9bc04e23d0fb95dac546
https://doi.org/10.1109/tgrs.2022.3175535
https://doi.org/10.1109/tgrs.2022.3175535
Publikováno v:
Analysis and Applications. 19:491-508
Recent studies have revealed that deep networks can learn transferable features that generalize well to novel tasks with little or unavailable labeled data for domain adaptation. However, justifying which components of the feature representations can
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed8433250052863817c3341ce338986d
https://doi.org/10.1142/s0219530520400035
https://doi.org/10.1142/s0219530520400035
Publikováno v:
CCIS
In this paper, a new domain adaptation learning method is proposed by jointing weighted balanced feature matching and instance reweighting based on kernel extreme learning machine (KELM). Specifically, for the weighted balance feature matching, it ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba4d7da5e0b3cb52fdd12ba29978aa40
https://doi.org/10.1109/ccis.2018.8691325
https://doi.org/10.1109/ccis.2018.8691325