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	Score
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation	Oct 8, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1	5
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation	Mar 30, 2023	Domain AdaptationMemorization	CodeCode Available	1	5
CoSDA: Continual Source-Free Domain Adaptation	Apr 13, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1	5
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation	Feb 20, 2020	Domain AdaptationPartial Domain Adaptation	CodeCode Available	1	5
Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection	Jul 23, 2024	Domain Adaptationobject-detection	CodeCode Available	1	5
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	5
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift	Jun 19, 2023	Domain AdaptationSemantic Segmentation	CodeCode Available	1	5
Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection	Mar 29, 2022	Domain AdaptationKnowledge Distillation	CodeCode Available	1	5
Balancing Discriminability and Transferability for Source-Free Domain Adaptation	Jun 16, 2022	Domain AdaptationSemantic Segmentation	CodeCode Available	1	5
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning	Mar 21, 2024	ClusteringContrastive Learning	CodeCode Available	1	5

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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