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 51–60 of 188 papers

Title	Date	Tasks	Status	Hype
Source-Free Domain Adaptation for Image Segmentation	Aug 6, 2021	Cardiac SegmentationDomain Adaptation	CodeCode Available	1
Generalized Source-free Domain Adaptation	Aug 3, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration	Jul 12, 2021	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation	Jun 18, 2021	Contrastive LearningDomain Adaptation	CodeCode Available	1
Transformer-Based Source-Free Domain Adaptation	May 28, 2021	Domain AdaptationKnowledge Distillation	CodeCode Available	1
Sign Segmentation with Changepoint-Modulated Pseudo-Labelling	Apr 28, 2021	Domain AdaptationSegmentation	CodeCode Available	1
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer	Dec 14, 2020	ClassificationDomain Adaptation	CodeCode Available	1
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments	Nov 27, 2020	Autonomous VehiclesDomain Adaptation	CodeCode Available	1
Casting a BAIT for Offline and Online Source-free Domain Adaptation	Oct 23, 2020	Domain AdaptationSource-Free Domain Adaptation	CodeCode Available	1
Tent: Fully Test-time Adaptation by Entropy Minimization	Jun 18, 2020	Domain AdaptationGeneral Classification	CodeCode Available	1

Show:10 25 50

← PrevPage 6 of 19Next →

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