Source-Free Domain Adaptation

Source-Free Domain Adaptation (SFDA) is a domain adaptation method in machine learning and computer vision where the goal is to adapt a pre-trained model to a new, target domain without access to the source domain data. This approach is advantageous in scenarios where sharing the source data is impractical due to privacy concerns, data size, or proprietary restrictions

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 71–80 of 188 papers

Title	Date	Tasks	Status
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation	Oct 27, 2023	DenoisingDomain Adaptation	—Unverified
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation	Jun 23, 2021	Domain AdaptationPseudo Label	—Unverified
Probability Distribution Alignment and Low-Rank Weight Decomposition for Source-Free Domain Adaptive Brain Decoding	Apr 12, 2025	Brain DecodingDomain Adaptation	—Unverified
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation	Jun 4, 2024	Domain AdaptationImputation	—Unverified
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation	Nov 27, 2023	DisentanglementDomain Adaptation	—Unverified
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets	Mar 7, 2024	Brain Computer Interfacechannel selection	—Unverified
ElimPCL: Eliminating Noise Accumulation with Progressive Curriculum Labeling for Source-Free Domain Adaptation	Mar 31, 2025	Domain AdaptationSource-Free Domain Adaptation	—Unverified
A Comprehensive Survey on Source-free Domain Adaptation	Feb 23, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified
Prior-guided Source-free Domain Adaptation for Human Pose Estimation	Aug 26, 2023	2D Human Pose EstimationDomain Adaptation	—Unverified
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement	Oct 3, 2024	Computational EfficiencyDisentanglement	—Unverified

Show:10 25 50

← PrevPage 8 of 19Next →

All datasets VisDA-2017 PACS Cityscapes to ACDC Cityscapes to Dark Zurich

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	RCL	Accuracy	93.2	—	Unverified
2	SFDA2++	Accuracy	89.6	—	Unverified
3	SPM	Accuracy	89.4	—	Unverified
4	SFDA2	Accuracy	88.1	—	Unverified
5	C-SFDA	Accuracy	87.8	—	Unverified
6	DaC	Accuracy	87.3	—	Unverified
7	SHOT++	Accuracy	87.3	—	Unverified
8	NRC	Accuracy	85.9	—	Unverified
9	G-SFDA	Accuracy	85.4	—	Unverified
10	SHOT	Accuracy	82.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SPM	Average Accuracy	86.7	—	Unverified
2	DRA	Average Accuracy	84	—	Unverified
3	NEL	Average Accuracy	72.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CMA	mIoU	69.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CMA	mIoU	53.6	—	Unverified