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 31–40 of 188 papers

Title	Date	Tasks	Status	Hype
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
Source-Free Adaptive Gaze Estimation by Uncertainty Reduction	Jan 1, 2023	Domain AdaptationGaze Estimation	CodeCode Available	1
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation	Jan 1, 2023	Domain AdaptationSemantic Segmentation	CodeCode Available	1
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
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition	Oct 11, 2022	Cross-Domain Facial Expression RecognitionDomain Adaptation	CodeCode Available	1
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning	Oct 5, 2022	Autonomous DrivingDomain Adaptation	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