Disentangled representation learning with causality for unsupervised domain adaptation
Most efforts in unsupervised domain adaptation (UDA) focus on learning the domain-
invariant representations between the two domains. However, such representations may still …
invariant representations between the two domains. However, such representations may still …
Riemannian representation learning for multi-source domain adaptation
Abstract Multi-Source Domain Adaptation (MSDA) aims at training a classification model that
achieves small target error, by leveraging labeled data from multiple source domains and …
achieves small target error, by leveraging labeled data from multiple source domains and …
Acan: a plug-and-play adaptive center-aligned network for unsupervised domain adaptation
Abstract Domain adaptation is an important topic due to its capability in transferring
knowledge from source domain to target domain. However, many existing domain …
knowledge from source domain to target domain. However, many existing domain …
DANE: A Dual-level Alignment Network with Ensemble Learning for Multi-Source Domain Adaptation
Multisource domain adaptation (MDA) aims to transfer knowledge from multiple labeled
source domains to an unlabeled target domain. However, the severe intradomain and …
source domains to an unlabeled target domain. However, the severe intradomain and …
Contrastive domain adaptation for time-series via temporal mixup
Unsupervised domain adaptation (UDA) has emerged as a powerful solution for the domain
shift problem via transferring the knowledge from a labeled source domain to a shifted …
shift problem via transferring the knowledge from a labeled source domain to a shifted …
Boosting unsupervised domain adaptation: A Fourier approach
By using unsupervised domain adaptation (UDA), knowledge is transferred from a label-rich
source domain to a target domain that contains relevant information but has no labels. Most …
source domain to a target domain that contains relevant information but has no labels. Most …
Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification
T Luo - Frontiers in Neuroscience, 2023 - frontiersin.org
Introduction Motor imagery electroencephalograph (MI-EEG) has attracted great attention in
constructing non-invasive brain-computer interfaces (BCIs) due to its low-cost and …
constructing non-invasive brain-computer interfaces (BCIs) due to its low-cost and …
Domain Adaptive 3D Shape Retrieval from Monocular Images
In this work, we address the novel and challenging problem of domain adaptive 3D shape
retrieval from single 2D images (DA-IBSR). While the existing image-based 3D shape …
retrieval from single 2D images (DA-IBSR). While the existing image-based 3D shape …
Dual-stream Feature Augmentation for Domain Generalization
Domain generalization (DG) task aims to learn a robust model from source domains that
could handle the out-of-distribution (OOD) issue. In order to improve the generalization …
could handle the out-of-distribution (OOD) issue. In order to improve the generalization …
[HTML][HTML] Generalizing source camera identification based on integral image optimization and constrained neural network
Y Wang, Q Sun, D Rong - Electronics, 2024 - mdpi.com
Source camera identification can verify whether two videos were shot by the same device,
which is of great significance in multimedia forensics. Most existing identification methods …
which is of great significance in multimedia forensics. Most existing identification methods …