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 1–25 of 188 papers

Title	Date	Tasks	Status	Hype	Score
A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts	Mar 27, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	3	5
Proxy Denoising for Source-Free Domain Adaptation	Jun 3, 2024	DenoisingDomain Adaptation	CodeCode Available	3	5
Unified Source-Free Domain Adaptation	Mar 12, 2024	Domain AdaptationLanguage Modelling	CodeCode Available	3	5
Source-Free Domain Adaptation with Frozen Multimodal Foundation Model	Nov 27, 2023	Domain AdaptationPrompt Learning	CodeCode Available	2	5
Source-Free Domain Adaptation for YOLO Object Detection	Sep 25, 2024	Domain AdaptationModel Selection	CodeCode Available	2	5
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning	Mar 21, 2024	ClusteringContrastive Learning	CodeCode Available	1	5
Generalized Source-free Domain Adaptation	Aug 3, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1	5
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation	Mar 7, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1	5
Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection	Jul 23, 2024	Domain Adaptationobject-detection	CodeCode Available	1	5
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation	Feb 20, 2020	Domain AdaptationPartial Domain Adaptation	CodeCode Available	1	5
GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation	Oct 22, 2024	Domain AdaptationGRAPH DOMAIN ADAPTATION	CodeCode Available	1	5
Anatomy-guided domain adaptation for 3D in-bed human pose estimation	Nov 22, 2022	3D Human Pose EstimationAnatomy	CodeCode Available	1	5
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation	Oct 8, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1	5
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation	Jul 3, 2024	Domain AdaptationImage Segmentation	CodeCode Available	1	5
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation	Jul 27, 2022	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1	5
Divide and Adapt: Active Domain Adaptation via Customized Learning	Jul 21, 2023	Active LearningDomain Adaptation	CodeCode Available	1	5
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation	Mar 9, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1	5
Confident Anchor-Induced Multi-Source Free Domain Adaptation	Dec 1, 2021	Domain AdaptationPseudo Label	CodeCode Available	1	5
BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation	Apr 6, 2022	ClusteringDomain Adaptation	CodeCode Available	1	5
CoSDA: Continual Source-Free Domain Adaptation	Apr 13, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1	5
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error	Aug 6, 2023	Domain AdaptationModel Selection	CodeCode Available	1	5
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation	Jan 6, 2025	Domain AdaptationImage Segmentation	CodeCode Available	1	5
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition	Oct 11, 2022	Cross-Domain Facial Expression RecognitionDomain Adaptation	CodeCode Available	1	5
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation	Dec 11, 2023	Domain AdaptationDomain Generalization	CodeCode Available	1	5
Balancing Discriminability and Transferability for Source-Free Domain Adaptation	Jun 16, 2022	Domain AdaptationSemantic Segmentation	CodeCode Available	1	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