3 papers
cs.LG2024
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation
Mohamed Ragab, Peiliang Gong, Emadeldeen Eldele +6
Source-free domain adaptation (SFDA) aims to adapt a model pre-trained on a labeled source domain to an unlabeled target domain without access to source data, preserving the source…
cs.CV2024
Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training
Wenyu Zhang, Li Shen, Chuan-Sheng Foo
Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to a related but unlabeled target domain. While the source model is a key…
cs.CV2024
Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias
Wenyu Zhang, Qingmu Liu, Felix Ong Wei Cong +2
Domain adaptation is a critical task in machine learning that aims to improve model performance on a target domain by leveraging knowledge from a related source domain. In this wor…