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 51–60 of 188 papers

Title	Date	Tasks	Status	Hype
Balancing Discriminability and Transferability for Source-Free Domain Adaptation	Jun 16, 2022	Domain AdaptationSemantic Segmentation	CodeCode Available	1
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation	Oct 8, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition	Mar 9, 2022	Action RecognitionDomain Adaptation	CodeCode Available	1
Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation	Jun 18, 2021	Contrastive LearningDomain Adaptation	CodeCode Available	1
Robust Source-Free Domain Adaptation for Fundus Image Segmentation	Oct 25, 2023	Domain AdaptationImage Segmentation	CodeCode Available	1
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer	Dec 14, 2020	ClassificationDomain Adaptation	CodeCode Available	1
Confident Anchor-Induced Multi-Source Free Domain Adaptation	Dec 1, 2021	Domain AdaptationPseudo Label	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
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation	Jul 3, 2024	Domain AdaptationImage Segmentation	CodeCode Available	1
Casting a BAIT for Offline and Online Source-free Domain Adaptation	Oct 23, 2020	Domain AdaptationSource-Free Domain 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