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 111–120 of 188 papers

Title	Date	Tasks	Status
Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation	Aug 1, 2021	Domain AdaptationSentence	—Unverified
Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation	Dec 8, 2022	Domain AdaptationMetric Learning	—Unverified
Semantic Image Segmentation: Two Decades of Research	Feb 13, 2023	Domain AdaptationDomain Generalization	—Unverified
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation	Jul 14, 2021	Domain AdaptationSource-Free Domain Adaptation	—Unverified
Semi-Supervised Transfer Boosting (SS-TrBoosting)	Dec 4, 2024	Domain AdaptationSemi-supervised Domain Adaptation	—Unverified
SepRep-Net: Multi-source Free Domain Adaptation via Model Separation And Reparameterization	Feb 13, 2024	Domain AdaptationSource-Free Domain Adaptation	—Unverified
Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping	Mar 8, 2022	Domain AdaptationSkull Stripping	—Unverified
Source-Free Domain Adaptation for Real-world Image Dehazing	Jul 14, 2022	Domain AdaptationImage Dehazing	—Unverified
Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers	May 23, 2023	Domain AdaptationSegmentation	—Unverified
Source-Free Domain Adaptation for Semantic Segmentation	Mar 30, 2021	Domain AdaptationSegmentation	—Unverified

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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