Source-free domain adaptation：无源域适应（SFDA）现有方法可以分为三个不同的类别。第一类方法明确地将...
Krishnan, Unsupervised pixel-level domain adaptation with generative...View more references Cited by (62) Source-free unsupervised adaptive segmentation for knee joint MRI 2024, Biomedical Signal Processing and Control Show abstract Source-free unsupervised domain adaptation: A survey 2024, Neural ...
Source-free domain adaptation via distri- bution estimation. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pages 7212–7222, 2022. 2 [5] Yuqi Fang, Pew-Thian Yap, Weili Lin, Hongtu Zhu, and Mingxia Liu. Source-free unsupervised domain adaptation: A sur...
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation Machine Learning, 113 (2024), pp. 3611-3631 CrossrefView in ScopusGoogle Scholar van Engelen and Hoos, 2019 van Engelen J.E., Hoos H.H. A survey on semi-supervised learning Machine...
unleashing the power of data tsunami: a comprehensive survey on data assessment and selection for instruction tuning of language models fantastic-data-engineering leveraging symmetry to accelerate learning of trajectory tracking controllers for free-flying robotic systems eqtrackingcontrol e-sort: empowering...
Paper tables with annotated results for Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey
For a fair evaluation, we compared our method with MCW14, which is similar to our approach, which addresses source-free multi-source domain adaptation. Additionally, we compared our supervised approach with the DECISION22 algorithm, which also tackles the problem of multi-source domain adaptation,...
To address the difficulty of collecting manually labeled training samples for object detection tasks, this paper proposes an unsupervised cross-domain object detection method that gradually adapts the model at pixel level and feature level. The existing pixel-level domain adaptive methods generate translat...
While Federated Learning (FL) provides a privacy-preserving approach to analyze sensitive data without centralizing training data, the field lacks an detai
Source-Free Unsupervised Domain Adaptation: Current research and future directions Ningyuan Zhang, ... Guangquan Zhang, in Neurocomputing, 2024 4.2.2 Hidden source information The source model often contains valuable information representing the source domain. Hidden source information methods tap into th...