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 26–50 of 188 papers

Title	Date	Tasks	Status	Hype
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation	Jul 27, 2022	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation	Mar 7, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Confident Anchor-Induced Multi-Source Free Domain Adaptation	Dec 1, 2021	Domain AdaptationPseudo Label	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
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation	Mar 9, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1
CoSDA: Continual Source-Free Domain Adaptation	Apr 13, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Balancing Discriminability and Transferability for Source-Free Domain Adaptation	Jun 16, 2022	Domain AdaptationSemantic Segmentation	CodeCode Available	1
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation	Oct 8, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning	Mar 21, 2024	ClusteringContrastive Learning	CodeCode Available	1
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift	Jun 19, 2023	Domain AdaptationSemantic Segmentation	CodeCode Available	1
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
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition	Mar 9, 2022	Action RecognitionDomain Adaptation	CodeCode Available	1
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation	May 9, 2022	ClusteringDiversity	CodeCode Available	1
Divide and Adapt: Active Domain Adaptation via Customized Learning	Jul 21, 2023	Active LearningDomain Adaptation	CodeCode Available	1
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning	Nov 12, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	1
ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation	Aug 4, 2023	Domain Adaptationimage-classification	CodeCode Available	1
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
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error	Aug 6, 2023	Domain AdaptationModel Selection	CodeCode Available	1
Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection	Jul 23, 2024	Domain Adaptationobject-detection	CodeCode Available	1
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation	Jan 6, 2025	Domain AdaptationImage Segmentation	CodeCode Available	1
SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis	Mar 28, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation	May 29, 2022	Domain AdaptationObject Recognition	CodeCode Available	1
GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation	Oct 22, 2024	Domain AdaptationGRAPH DOMAIN ADAPTATION	CodeCode Available	1
Tent: Fully Test-time Adaptation by Entropy Minimization	Jun 18, 2020	Domain AdaptationGeneral Classification	CodeCode Available	1
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer	Dec 14, 2020	ClassificationDomain 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