SOTAVerified

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.

Further readings:

( Image credit: Unsupervised Image-to-Image Translation Networks )

Papers

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Showing 1–10 of 6439 papers

TitleStatusHype
A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique—0
Domain Borders Are There to Be Crossed With Federated Few-Shot AdaptationCode0
The Bayesian Approach to Continual Learning: An Overview—0
An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation—0
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection—0
YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries—0
CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble FusionCode0
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning—0
UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather ConditionsCode1
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud RecognitionCode0
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All datasetsOffice-31SYNTHIA-to-CityscapesGTA5 to CityscapesOffice-HomeVisDA2017ImageCLEF-DACityscapes to ACDCMNIST-to-USPSSVHN-to-MNISTUSPS-to-MNISTSVNH-to-MNISTMoLane

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HALOmIoU78.1—Unverified
2ILM-ASSLmIoU76.6—Unverified
3DCFmIoU69.3—Unverified
4HRDA+PiPamIoU68.2—Unverified
5MICmIoU67.3—Unverified
6FREDOM - TransformermIoU67—Unverified
7HRDAmIoU65.8—Unverified
8SePiComIoU64.3—Unverified
9MIC + Guidance TrainingmIoU63.8—Unverified
10DAFormer + ProCSTmIoU61.6—Unverified