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 121–130 of 188 papers

Title	Date	Tasks	Status	Hype
Source-Free Video Domain Adaptation With Spatial-Temporal-Historical Consistency Learning	Jan 1, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0
Source-Free Domain Adaptation for Question Answering with Masked Self-training	Dec 19, 2022	Domain AdaptationQuestion Answering	CodeCode Available	0
Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation	Dec 15, 2022	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0
Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation	Dec 15, 2022	Domain AdaptationRepresentation Learning	CodeCode Available	0
Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation	Dec 8, 2022	Domain AdaptationMetric Learning	—Unverified	0
Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain Adaptation	Dec 7, 2022	Domain AdaptationPseudo Label	CodeCode Available	0
Anatomy-guided domain adaptation for 3D in-bed human pose estimation	Nov 22, 2022	3D Human Pose EstimationAnatomy	CodeCode Available	1
ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation	Nov 21, 2022	Domain AdaptationImage Segmentation	CodeCode Available	1
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning	Nov 12, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	1
TTTFlow: Unsupervised Test-Time Training with Normalizing Flow	Oct 20, 2022	image-classificationImage Classification	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