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 101–110 of 188 papers

Title	Date	Tasks	Status	Hype
Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer	Mar 31, 2023	Domain AdaptationImage Super-Resolution	CodeCode Available	1
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation	Mar 30, 2023	Domain AdaptationMemorization	CodeCode Available	1
SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis	Mar 28, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts	Mar 27, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	3
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation	Mar 25, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	0
TempT: Temporal consistency for Test-time adaptation	Mar 19, 2023	Domain AdaptationFacial Expression Recognition	—Unverified	0
Upcycling Models under Domain and Category Shift	Mar 13, 2023	ClusteringDomain Adaptation	CodeCode Available	1
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation	Mar 9, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation	Mar 7, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
A Comprehensive Survey on Source-free Domain Adaptation	Feb 23, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0

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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