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
Test-time Batch Statistics Calibration for Covariate Shift	Oct 6, 2021	Domain AdaptationDomain Generalization	—Unverified
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
Transcending Domains through Text-to-Image Diffusion: A Source-Free Approach to Domain Adaptation	Oct 2, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering	Sep 1, 2023	ClusteringDomain Adaptation	—Unverified
Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data	Jul 14, 2021	Domain AdaptationPrivacy Preserving	—Unverified
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective	Jan 1, 2024	DiversityDomain 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
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision	Aug 13, 2023	Contrastive LearningDomain Adaptation	—Unverified
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey	Dec 6, 2021	Domain AdaptationDomain Generalization	—Unverified
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation	Nov 24, 2024	Domain AdaptationSource-Free Domain Adaptation	—Unverified

Show:10 25 50

← PrevPage 14 of 19Next →

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