Domain Adaptation

Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1–10 of 6439 papers

Title	Date	Tasks	Status	Hype
A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique	Jul 17, 2025	Domain AdaptationPrivacy Preserving	—Unverified	0
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation	Jul 14, 2025	Domain AdaptationFederated Learning	CodeCode Available	0
The Bayesian Approach to Continual Learning: An Overview	Jul 11, 2025	Bayesian Inferenceclass-incremental learning	—Unverified	0
An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation	Jul 11, 2025	Domain Adaptation	—Unverified	0
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection	Jul 10, 2025	Domain AdaptationFew-Shot Learning	—Unverified	0
YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries	Jul 7, 2025	Autonomous NavigationDomain Adaptation	—Unverified	0
CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion	Jul 4, 2025	Domain AdaptationPerson Re-Identification	CodeCode Available	0
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning	Jul 2, 2025	Depth EstimationDomain Adaptation	—Unverified	0
UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions	Jul 1, 2025	Domain AdaptationObject Tracking	CodeCode Available	1
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition	Jun 26, 2025	Contrastive LearningDomain Adaptation	CodeCode Available	0

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All datasets Office-31 SYNTHIA-to-Cityscapes GTA5 to Cityscapes Office-Home VisDA2017 ImageCLEF-DA Cityscapes to ACDC MNIST-to-USPS SVHN-to-MNIST USPS-to-MNIST SVNH-to-MNIST MoLane

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Mean teacher	Accuracy	99.18	—	Unverified
2	SHOT	Accuracy	98.9	—	Unverified
3	DFA-MCD	Accuracy	98.9	—	Unverified
4	FAMCD	Accuracy	98.76	—	Unverified
5	DFA-ENT	Accuracy	98.2	—	Unverified
6	CyCleGAN (Light-weight Calibrator)	Accuracy	97.5	—	Unverified
7	MCD	Accuracy	95.8	—	Unverified
8	PFA	Accuracy	93.9	—	Unverified
9	MSTN	Accuracy	93.3	—	Unverified
10	FACT	Accuracy	90.6	—	Unverified