A review of single-source deep unsupervised visual domain adaptation
Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …
Pseudo labels for unsupervised domain adaptation: A review
Y Li, L Guo, Y Ge - Electronics, 2023 - mdpi.com
Conventional machine learning relies on two presumptions:(1) the training and testing
datasets follow the same independent distribution, and (2) an adequate quantity of samples …
datasets follow the same independent distribution, and (2) an adequate quantity of samples …
Domain impression: A source data free domain adaptation method
VK Kurmi, VK Subramanian… - Proceedings of the …, 2021 - openaccess.thecvf.com
Unsupervised Domain adaptation methods solve the adaptation problem for an unlabeled
target set, assuming that the source dataset is available with all labels. However, the …
target set, assuming that the source dataset is available with all labels. However, the …
Tvt: Transferable vision transformer for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learnt from a
labeled source domain to an unlabeled target domain. Previous work is mainly built upon …
labeled source domain to an unlabeled target domain. Previous work is mainly built upon …
Metacorrection: Domain-aware meta loss correction for unsupervised domain adaptation in semantic segmentation
Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled
source domain to the unlabeled target domain. Existing self-training based UDA approaches …
source domain to the unlabeled target domain. Existing self-training based UDA approaches …
Metaalign: Coordinating domain alignment and classification for unsupervised domain adaptation
For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many
approaches align the source and target domains in the feature space by adversarial …
approaches align the source and target domains in the feature space by adversarial …
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 …
Conditional contrastive domain generalization for fault diagnosis
Data-driven fault diagnosis plays a key role in stability and reliability of operations in modern
industries. Recently, deep learning has achieved remarkable performance in fault …
industries. Recently, deep learning has achieved remarkable performance in fault …
Toalign: Task-oriented alignment for unsupervised domain adaptation
Unsupervised domain adaptive classifcation intends to improve the classifcation
performance on unlabeled target domain. To alleviate the adverse effect of domain shift …
performance on unlabeled target domain. To alleviate the adverse effect of domain shift …
Gradient distribution alignment certificates better adversarial domain adaptation
The latest heuristic for handling the domain shift in unsupervised domain adaptation tasks is
to reduce the data distribution discrepancy using adversarial learning. Recent studies …
to reduce the data distribution discrepancy using adversarial learning. Recent studies …