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Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for
  Source-Free Unsupervised Domain Adaptation

Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation

29 March 2021
Waqar Ahmed
Pietro Morerio
Vittorio Murino
ArXiv (abs)PDFHTML

Papers citing "Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation"

4 / 4 papers shown
Title
Model Adaptation: Unsupervised Domain Adaptation without Source Data
Model Adaptation: Unsupervised Domain Adaptation without Source DataComputer Vision and Pattern Recognition (CVPR), 2020
Rui Li
Qianfen Jiao
Wenming Cao
Hau-San Wong
Si Wu
OOD
718
555
0
26 Feb 2025
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised
  Video Anomaly Detection
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly DetectionIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2023
Anas Al-Lahham
Nurbek Tastan
Muhammad Zaigham Zaheer
Karthik Nandakumar
284
23
0
26 Oct 2023
Complementary Domain Adaptation and Generalization for Unsupervised
  Continual Domain Shift Learning
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift LearningIEEE International Conference on Computer Vision (ICCV), 2023
Won-Yong Cho
Jinha Park
Taesup Kim
CLL
261
11
0
28 Mar 2023
Uncertainty-guided Source-free Domain Adaptation
Uncertainty-guided Source-free Domain AdaptationEuropean Conference on Computer Vision (ECCV), 2022
Subhankar Roy
Martin Trapp
Andrea Pilzer
Arno Solin
Andrii Zadaianchuk
Elisa Ricci
Arno Solin
EDLTTAUQLMUQCV
253
78
0
16 Aug 2022
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