Transfer adaptation learning: A decade survey
The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …
environment. Domain is referred to as the state of the world at a certain moment. A research …
Deepfake detection using deep learning methods: A systematic and comprehensive review
Deep Learning (DL) has been effectively utilized in various complicated challenges in
healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung …
healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung …
Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer
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 …
different well-labeled source domain to a new unlabeled target domain. Most existing UDA …
Cross-domain gradient discrepancy minimization for unsupervised domain adaptation
Abstract Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned
from a well-labeled source domain to an unlabled target domain. Recently, adversarial …
from a well-labeled source domain to an unlabled target domain. Recently, adversarial …
Maximum density divergence for domain adaptation
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 …
well-labeled source domain to an unlabeled target domain where the two domains have …
Active learning for domain adaptation: An energy-based approach
Unsupervised domain adaptation has recently emerged as an effective paradigm for
generalizing deep neural networks to new target domains. However, there is still enormous …
generalizing deep neural networks to new target domains. However, there is still enormous …
Divergence-agnostic unsupervised domain adaptation by adversarial attacks
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 …
existing data fails to generalize well to the data sampled from other distributions. To tackle …
Transferable semantic augmentation for domain adaptation
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 …
label-rich source domain to a related but unlabeled target domain. Most existing domain …
Discriminative manifold distribution alignment for domain adaptation
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 …
transferring knowledge from related source domains. In order to learn a discriminative and …
Federated deep learning for anomaly detection in the internet of things
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 …
because IoT devices produce and transmit sensitive information through the regular internet …