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 51–75 of 188 papers

Title	Date	Tasks	Status	Hype	Score
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
TTTFlow: Unsupervised Test-Time Training with Normalizing Flow	Oct 20, 2022	image-classificationImage Classification	CodeCode Available	1	5
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition	Mar 9, 2022	Action RecognitionDomain Adaptation	CodeCode Available	1	5
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation	Sep 19, 2023	Brain SegmentationDomain Adaptation	CodeCode Available	1	5
Sign Segmentation with Changepoint-Modulated Pseudo-Labelling	Apr 28, 2021	Domain AdaptationSegmentation	CodeCode Available	1	5
Confident Anchor-Induced Multi-Source Free Domain Adaptation	Dec 1, 2021	Domain AdaptationPseudo Label	CodeCode Available	1	5
Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights	Jul 10, 2024	Domain Adaptationobject-detection	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
Tent: Fully Test-time Adaptation by Entropy Minimization	Jun 18, 2020	Domain AdaptationGeneral Classification	CodeCode Available	1	5
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation	Mar 9, 2023	Contrastive LearningDomain 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
Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation	Jan 31, 2023	ClusteringContrastive Learning	CodeCode Available	0	5
Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation	Apr 3, 2023	AllDomain Adaptation	CodeCode Available	0	5
SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation	Oct 13, 2023	Domain AdaptationSelf-Supervised Learning	CodeCode Available	0	5
FACT: Federated Adversarial Cross Training	Jun 1, 2023	Domain AdaptationFederated Learning	CodeCode Available	0	5
SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency	Sep 18, 2024	Domain AdaptationDomain Generalization	CodeCode Available	0	5
Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation	Dec 15, 2022	Domain AdaptationRepresentation Learning	CodeCode Available	0	5
CNG-SFDA:Clean-and-Noisy Region Guided Online-Offline Source-Free Domain Adaptation	Jan 26, 2024	Domain AdaptationPseudo Label	CodeCode Available	0	5
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning	May 28, 2024	Domain AdaptationInstruction Following	CodeCode Available	0	5
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing	Aug 1, 2021	Domain AdaptationNegation	CodeCode Available	0	5
EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse Principles	Jul 23, 2024	Domain AdaptationFine-Grained Visual Categorization	CodeCode Available	0	5
1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track	Jan 12, 2023	Domain AdaptationDomain Generalization	CodeCode Available	0	5
Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation	May 30, 2025	Data AugmentationDomain Adaptation	CodeCode Available	0	5
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data	Apr 17, 2025	Data AugmentationDiversity	CodeCode Available	0	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