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 101–125 of 188 papers

Title	Date	Tasks	Status	Hype
Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer	Mar 31, 2023	Domain AdaptationImage Super-Resolution	CodeCode Available	1
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation	Mar 30, 2023	Domain AdaptationMemorization	CodeCode Available	1
SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis	Mar 28, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts	Mar 27, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	3
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation	Mar 25, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	0
TempT: Temporal consistency for Test-time adaptation	Mar 19, 2023	Domain AdaptationFacial Expression Recognition	—Unverified	0
Upcycling Models under Domain and Category Shift	Mar 13, 2023	ClusteringDomain Adaptation	CodeCode Available	1
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation	Mar 9, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation	Mar 7, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
A Comprehensive Survey on Source-free Domain Adaptation	Feb 23, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0
In Search for a Generalizable Method for Source Free Domain Adaptation	Feb 13, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0
Semantic Image Segmentation: Two Decades of Research	Feb 13, 2023	Domain AdaptationDomain Generalization	—Unverified	0
When Source-Free Domain Adaptation Meets Learning with Noisy Labels	Jan 31, 2023	Domain AdaptationLearning with noisy labels	—Unverified	0
Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation	Jan 31, 2023	ClusteringContrastive Learning	CodeCode Available	0
Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation	Jan 20, 2023	ClusteringDomain Adaptation	—Unverified	0
1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track	Jan 12, 2023	Domain AdaptationDomain Generalization	CodeCode Available	0
SSDA: Secure Source-Free Domain Adaptation	Jan 1, 2023	Backdoor AttackDomain Adaptation	CodeCode Available	0
Source-Free Adaptive Gaze Estimation by Uncertainty Reduction	Jan 1, 2023	Domain AdaptationGaze Estimation	CodeCode Available	1
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation	Jan 1, 2023	Active LearningDomain Adaptation	—Unverified	0
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation	Jan 1, 2023	Domain AdaptationSemantic Segmentation	CodeCode Available	1
Source-Free Video Domain Adaptation With Spatial-Temporal-Historical Consistency Learning	Jan 1, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0
Source-Free Domain Adaptation for Question Answering with Masked Self-training	Dec 19, 2022	Domain AdaptationQuestion Answering	CodeCode Available	0
Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation	Dec 15, 2022	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0
Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation	Dec 15, 2022	Domain AdaptationRepresentation Learning	CodeCode Available	0
Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation	Dec 8, 2022	Domain AdaptationMetric Learning	—Unverified	0

Show:10 25 50

← PrevPage 5 of 8Next →

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