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

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Showing 91–100 of 188 papers

Title	Date	Tasks	Status	Score
Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation	May 30, 2025	Data AugmentationDomain Adaptation	CodeCode Available	5
SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation	Oct 13, 2023	Domain AdaptationSelf-Supervised Learning	CodeCode Available	5
Source-Free Domain Adaptation for Question Answering with Masked Self-training	Dec 19, 2022	Domain AdaptationQuestion Answering	CodeCode Available	5
Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training	May 5, 2024	Domain AdaptationLanguage Modelling	CodeCode Available	5
Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces	May 27, 2023	Brain Computer InterfaceDomain Adaptation	CodeCode Available	5
Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology	Apr 29, 2024	Contrastive LearningDomain Adaptation	CodeCode Available	5
Source-Free Domain Adaptation via Distribution Estimation	Apr 24, 2022	Domain AdaptationPrivacy Preserving	CodeCode Available	5
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation	Mar 25, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	5
SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network	May 23, 2024	CPUDomain Adaptation	CodeCode Available	5
SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation	Jun 11, 2025	Domain AdaptationImage Segmentation	CodeCode Available	5

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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