Zobrazeno 1 - 10
of 568
pro vyhledávání: '"domain adaptation (da)"'
Autor:
DAN Yufang, TAO Jianwen
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 3, Pp 674-692 (2024)
Domain adaptation (DA) aims to solve the problem of inconsistent distribution between training dataset and test dataset, which has attracted extensive attention. Most of the existing DA methods solve this problem by the maximum mean discrepancy (MMD)
Externí odkaz:
https://doaj.org/article/5cd3846ea4f9483eb476d175a3eafa80
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 20344-20354 (2024)
Deep-learning-based multilabel remote sensing image annotation (MLRSIA) is receiving increasing attention in recent years. MLRSIA needs a large volume of labeled samples for effective training of the deep models. However, the scarcity of labeled samp
Externí odkaz:
https://doaj.org/article/99445c1cba884102988e24aae9d2606f
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13016-13029 (2024)
Environment and population are closely linked, but their interactions remain challenging to assess. To fill this gap, modeling the environment at a fine resolution brings a significant value, if combined with population-based studies. This is particu
Externí odkaz:
https://doaj.org/article/b2a457bc4f19472fa2b5117de51914c3
Autor:
Pedro Juan Soto Vega, Gilson Alexandre Ostwald Pedro da Costa, Mabel Ximena Ortega Adarme, Jose David Bermudez Castro, Raul Queiroz Feitosa
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 10264-10278 (2023)
Domain adaptation has proven to be suitable for alleviating domain discrepancies, which hinder the generalization capacity of classifiers. Among a few alternatives, domain adaptation techniques that align features in a domain-agnostic space through a
Externí odkaz:
https://doaj.org/article/e5d40fc1c31047f5bd2810759ee1d29a
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 806-817 (2023)
To construct a more effective model with good generalization performance for inter-site autism spectrum disorder (ASD) diagnosis, domain adaptation based ASD diagnostic models are proposed to alleviate the inter-site heterogeneity. However, most exis
Externí odkaz:
https://doaj.org/article/2f8a530ba72b4f5c8816584a96dfd718
Autor:
Mikhail Sokolov, Christopher Henry, Joni Storie, Christopher Storie, Victor Alhassan, Mathieu Turgeon-Pelchat
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 482-492 (2023)
Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation (DA) probl
Externí odkaz:
https://doaj.org/article/76d2c6ce3cdc466bb4b02b96172bd206
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 9842-9859 (2022)
Traditional remote sensing (RS) image classification methods heavily rely on labeled samples for model training. When labeled samples are unavailable or labeled samples have different distributions from that of the samples to be classified, the class
Externí odkaz:
https://doaj.org/article/c53395b7ebc8451d8ddf6ee932d28db0
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4797-4808 (2022)
Multimodally distributed data is very common in remote sensing images, such as hyperspectral images (HSIs). It is important to capture the local manifold structure while preserving the global discriminant information in the multimodal data. In this a
Externí odkaz:
https://doaj.org/article/85ae20abed8c4c79b5e373a0952ac5ba
Autor:
Shuangmei Zhao, Haitao Lang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 8038-8048 (2022)
This study aims at improving fine-grained ship classification performance under the condition that there is no labeled samples available in SAR domain (target domain) by transferring the knowledge from optical remote sensing (ORS) domain (source doma
Externí odkaz:
https://doaj.org/article/bb3dd3403f324872af0ecbcb669b8f21