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	Score
SSDA: Secure Source-Free Domain Adaptation	Jan 1, 2023	Backdoor AttackDomain Adaptation	CodeCode Available	5
RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation	Aug 22, 2022	Data AugmentationDomain Adaptation	CodeCode Available	5
Unveiling the Unknown: Unleashing the Power of Unknown to Known in Open-Set Source-Free Domain Adaptation	Jan 1, 2024	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	5
Variational Model Perturbation for Source-Free Domain Adaptation	Oct 19, 2022	Bayesian InferenceDomain Adaptation	CodeCode Available	5
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood Context	Dec 18, 2024	Continual LearningContrastive Learning	CodeCode Available	5
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather	Mar 20, 2024	Autonomous VehiclesDomain Adaptation	CodeCode Available	5
AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation	Jul 21, 2021	Domain AdaptationSegmentation	—Unverified	0
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation	Sep 9, 2021	Domain AdaptationPseudo Label	—Unverified	0
Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation	Aug 1, 2021	Domain AdaptationSentence	—Unverified	0
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation	Nov 27, 2023	DisentanglementDomain 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