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 91–100 of 188 papers

Title	Date	Tasks	Status	Hype
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift	Jun 19, 2023	Domain AdaptationSemantic Segmentation	CodeCode Available	1
Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning	Jun 2, 2023	Domain AdaptationSegmentation	CodeCode Available	1
FACT: Federated Adversarial Cross Training	Jun 1, 2023	Domain AdaptationFederated Learning	CodeCode Available	0
Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces	May 27, 2023	Brain Computer InterfaceDomain Adaptation	CodeCode Available	0
Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers	May 23, 2023	Domain AdaptationSegmentation	—Unverified	0
Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment	May 22, 2023	Domain AdaptationPseudo Label	—Unverified	0
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation	May 13, 2023	Domain AdaptationKnowledge Distillation	—Unverified	0
CoSDA: Continual Source-Free Domain Adaptation	Apr 13, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Source-free Domain Adaptation Requires Penalized Diversity	Apr 6, 2023	DiversityDomain Adaptation	—Unverified	0
Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation	Apr 3, 2023	AllDomain Adaptation	CodeCode Available	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