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 131–140 of 188 papers

Title	Date	Tasks	Status	Hype
Variational Model Perturbation for Source-Free Domain Adaptation	Oct 19, 2022	Bayesian InferenceDomain Adaptation	CodeCode Available	0
Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation	Oct 14, 2022	ClusteringDomain Adaptation	CodeCode Available	0
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition	Oct 11, 2022	Cross-Domain Facial Expression RecognitionDomain Adaptation	CodeCode Available	1
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning	Oct 5, 2022	Autonomous DrivingDomain Adaptation	CodeCode Available	1
RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation	Aug 22, 2022	Data AugmentationDomain Adaptation	CodeCode Available	0
Uncertainty-guided Source-free Domain Adaptation	Aug 16, 2022	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation	Jul 27, 2022	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Feed-Forward Latent Domain Adaptation	Jul 15, 2022	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0
Source-Free Domain Adaptation for Real-world Image Dehazing	Jul 14, 2022	Domain AdaptationImage Dehazing	—Unverified	0
Balancing Discriminability and Transferability for Source-Free Domain Adaptation	Jun 16, 2022	Domain AdaptationSemantic Segmentation	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