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 21–30 of 188 papers

Title	Date	Tasks	Status	Hype
Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning	Jul 19, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data	Jul 14, 2023	Domain AdaptationImputation	CodeCode Available	1
Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher	Jul 14, 2023	Image SegmentationSemantic Segmentation	CodeCode Available	1
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift	Jun 19, 2023	Domain AdaptationSemantic Segmentation	CodeCode Available	1
Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning	Jun 2, 2023	Domain AdaptationSegmentation	CodeCode Available	1
CoSDA: Continual Source-Free Domain Adaptation	Apr 13, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
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
Upcycling Models under Domain and Category Shift	Mar 13, 2023	ClusteringDomain Adaptation	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