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 71–80 of 188 papers

Title	Date	Tasks	Status	Hype
SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation	Oct 13, 2023	Domain AdaptationSelf-Supervised Learning	CodeCode Available	0
Transcending Domains through Text-to-Image Diffusion: A Source-Free Approach to Domain Adaptation	Oct 2, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified	0
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation	Sep 19, 2023	Brain SegmentationDomain Adaptation	CodeCode Available	1
Better Practices for Domain Adaptation	Sep 7, 2023	BenchmarkingDomain Adaptation	—Unverified	0
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering	Sep 1, 2023	ClusteringDomain Adaptation	—Unverified	0
Local-Global Pseudo-label Correction for Source-free Domain Adaptive Medical Image Segmentation	Aug 28, 2023	Domain AdaptationImage Segmentation	—Unverified	0
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation	Aug 27, 2023	DisentanglementDomain Adaptation	—Unverified	0
Prior-guided Source-free Domain Adaptation for Human Pose Estimation	Aug 26, 2023	2D Human Pose EstimationDomain Adaptation	—Unverified	0
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision	Aug 13, 2023	Contrastive LearningDomain Adaptation	—Unverified	0
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error	Aug 6, 2023	Domain AdaptationModel Selection	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