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 121–130 of 188 papers

Title	Date	Tasks	Status
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering	Sep 1, 2023	ClusteringDomain Adaptation	—Unverified
Local-Global Pseudo-label Correction for Source-free Domain Adaptive Medical Image Segmentation	Aug 28, 2023	Domain AdaptationImage Segmentation	—Unverified
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation	Aug 27, 2023	DisentanglementDomain Adaptation	—Unverified
Prior-guided Source-free Domain Adaptation for Human Pose Estimation	Aug 26, 2023	2D Human Pose EstimationDomain Adaptation	—Unverified
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision	Aug 13, 2023	Contrastive LearningDomain Adaptation	—Unverified
Consistency Regularization for Generalizable Source-free Domain Adaptation	Aug 3, 2023	Domain AdaptationPseudo Label	—Unverified
Feed-Forward Source-Free Domain Adaptation via Class Prototypes	Jul 20, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified
Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples	Jul 19, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified
Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation	Jul 3, 2023	Domain AdaptationImage-to-Image Translation	—Unverified
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation	Jul 1, 2023	DecoderDomain Adaptation	CodeCode Available

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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