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 11–20 of 188 papers

Title	Date	Tasks	Status	Hype
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning	Nov 12, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	1
Anatomy-guided domain adaptation for 3D in-bed human pose estimation	Nov 22, 2022	3D Human Pose EstimationAnatomy	CodeCode Available	1
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation	Dec 11, 2023	Domain AdaptationDomain Generalization	CodeCode Available	1
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation	Jul 3, 2024	Domain AdaptationImage Segmentation	CodeCode Available	1
Confident Anchor-Induced Multi-Source Free Domain Adaptation	Dec 1, 2021	Domain AdaptationPseudo Label	CodeCode Available	1
CoSDA: Continual Source-Free Domain Adaptation	Apr 13, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition	Oct 11, 2022	Cross-Domain Facial Expression RecognitionDomain Adaptation	CodeCode Available	1
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation	Mar 9, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1
BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation	Apr 6, 2022	ClusteringDomain Adaptation	CodeCode Available	1
Balancing Discriminability and Transferability for Source-Free Domain Adaptation	Jun 16, 2022	Domain AdaptationSemantic Segmentation	CodeCode Available	1

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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