Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung …
J Liang, D Hu, Y Wang, R He… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA …
Z Du, J Li, H Su, L Zhu, K Lu - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
Abstract Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabled target domain. Recently, adversarial …
J Li, E Chen, Z Ding, L Zhu, K Lu… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have …
Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous …
Conventional machine learning algorithms suffer the problem that the model trained on existing data fails to generalize well to the data sampled from other distributions. To tackle …
Abstract Domain adaptation has been widely explored by transferring the knowledge from a label-rich source domain to a related but unlabeled target domain. Most existing domain …
Domain adaptation (DA) aims to accomplish tasks on unlabeled target data by learning and transferring knowledge from related source domains. In order to learn a discriminative and …
X Wang, Y Wang, Z Javaheri, L Almutairi… - Computers and …, 2023 - Elsevier
Privacy has emerged as a top worry as a result of the development of zero-day hacks because IoT devices produce and transmit sensitive information through the regular internet …