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 61–70 of 188 papers

Title	Date	Tasks	Status	Hype
Unveiling the Unknown: Unleashing the Power of Unknown to Known in Open-Set Source-Free Domain Adaptation	Jan 1, 2024	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	0
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
Target-agnostic Source-free Domain Adaptation for Regression Tasks	Dec 1, 2023	Domain AdaptationPrediction	—Unverified	0
Self-training solutions for the ICCV 2023 GeoNet Challenge	Nov 28, 2023	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	0
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation	Nov 27, 2023	DisentanglementDomain Adaptation	—Unverified	0
Source-Free Domain Adaptation with Frozen Multimodal Foundation Model	Nov 27, 2023	Domain AdaptationPrompt Learning	CodeCode Available	2
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation	Oct 31, 2023	Active LearningDomain Adaptation	—Unverified	0
Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition	Oct 30, 2023	Autonomous VehiclesDomain Adaptation	—Unverified	0
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation	Oct 27, 2023	DenoisingDomain Adaptation	—Unverified	0
Robust Source-Free Domain Adaptation for Fundus Image Segmentation	Oct 25, 2023	Domain AdaptationImage Segmentation	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