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 151–175 of 188 papers

Title	Date	Tasks	Status	Hype
Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping	Mar 8, 2022	Domain AdaptationSkull Stripping	—Unverified	0
Cleaning Noisy Labels by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation	Feb 15, 2022	Domain AdaptationEnsemble Learning	—Unverified	0
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation	Feb 1, 2022	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	0
Exploring Domain-Invariant Parameters for Source Free Domain Adaptation	Jan 1, 2022	Domain AdaptationPrivacy Preserving	—Unverified	0
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey	Dec 6, 2021	Domain AdaptationDomain Generalization	—Unverified	0
Confident Anchor-Induced Multi-Source Free Domain Adaptation	Dec 1, 2021	Domain AdaptationPseudo Label	CodeCode Available	1
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation	Oct 8, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Test-time Batch Statistics Calibration for Covariate Shift	Oct 6, 2021	Domain AdaptationDomain Generalization	—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
A Comparison of Strategies for Source-Free Domain Adaptation	Aug 17, 2021	Active LearningData Augmentation	—Unverified	0
Source-Free Domain Adaptation for Image Segmentation	Aug 6, 2021	Cardiac SegmentationDomain Adaptation	CodeCode Available	1
Generalized Source-free Domain Adaptation	Aug 3, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
YNU-HPCC at SemEval-2021 Task 10: Using a Transformer-based Source-Free Domain Adaptation Model for Semantic Processing	Aug 1, 2021	Domain AdaptationNER	—Unverified	0
MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain Adaptation	Aug 1, 2021	Domain AdaptationNegation	—Unverified	0
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation	Aug 1, 2021	Active LearningData Augmentation	—Unverified	0
IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes	Aug 1, 2021	Data AugmentationDomain Adaptation	—Unverified	0
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing	Aug 1, 2021	Domain AdaptationNegation	CodeCode Available	0
BLCUFIGHT at SemEval-2021 Task 10: Novel Unsupervised Frameworks For Source-Free Domain Adaptation	Aug 1, 2021	AttributeDomain Adaptation	—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
AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation	Jul 21, 2021	Domain AdaptationSegmentation	—Unverified	0
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation	Jul 14, 2021	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0
Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data	Jul 14, 2021	Domain AdaptationPrivacy Preserving	—Unverified	0
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration	Jul 12, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation	Jun 23, 2021	Domain AdaptationPseudo Label	—Unverified	0
A Curriculum-style Self-training Approach for Source-Free Semantic Segmentation	Jun 22, 2021	Domain AdaptationRepresentation Learning	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