Cdtrans: Cross-domain transformer for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to a different unlabeled target domain. Most existing UDA methods focus on …
source domain to a different unlabeled target domain. Most existing UDA methods focus on …
Domain adaptation via prompt learning
Unsupervised domain adaptation (UDA) aims to adapt models learned from a well-
annotated source domain to a target domain, where only unlabeled samples are given …
annotated source domain to a target domain, where only unlabeled samples are given …
C-sfda: A curriculum learning aided self-training framework for efficient source free domain adaptation
N Karim, NC Mithun, A Rajvanshi… - Proceedings of the …, 2023 - openaccess.thecvf.com
Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a
labeled source domain to an unlabeled target domain. In contrast to UDA, source-free …
labeled source domain to an unlabeled target domain. In contrast to UDA, source-free …
Domain adaptation with auxiliary target domain-oriented classifier
Abstract Domain adaptation (DA) aims to transfer knowledge from a label-rich but
heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and …
heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and …
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 …
Towards domain adaptation with open-set target data: Review of theory and computer vision applications R1# C1
Open-set domain adaptation is a developing and practical solution to training deep networks
using unlabeled data which have been collected among unknown data and are under …
using unlabeled data which have been collected among unknown data and are under …
Semantic concentration for domain adaptation
Abstract Domain adaptation (DA) paves the way for label annotation and dataset bias issues
by the knowledge transfer from a label-rich source domain to a related but unlabeled target …
by the knowledge transfer from a label-rich source domain to a related but unlabeled target …
Make the u in uda matter: Invariant consistency learning for unsupervised domain adaptation
Abstract Domain Adaptation (DA) is always challenged by the spurious correlation between
the domain-invariant features (eg, class identity) and the domain-specific ones (eg …
the domain-invariant features (eg, class identity) and the domain-specific ones (eg …
An unsupervised domain adaptation approach with enhanced transferability and discriminability for bearing fault diagnosis under few-shot samples
W Ma, Y Zhang, L Ma, R Liu, S Yan - Expert Systems with Applications, 2023 - Elsevier
As a key component widely used in electric multiple units (EMU), fault diagnosis of EMU
bearing is an important link. Typically, labeled data from different conditions provides the …
bearing is an important link. Typically, labeled data from different conditions provides the …
Bi-classifier determinacy maximization for unsupervised domain adaptation
Unsupervised domain adaptation challenges the problem of transferring knowledge from a
well-labelled source domain to an unlabelled target domain. Recently, adversarial learning …
well-labelled source domain to an unlabelled target domain. Recently, adversarial learning …