SOTAVerified

Multi-target Domain Adaptation

The idea of Multi-target Domain Adaptation is to adapt a model from a single labelled source domain to multiple unlabelled target domains.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 39 papers

TitleStatusHype
Merge-Friendly Post-Training Quantization for Multi-Target Domain AdaptationCode0
GrInAdapt: Scaling Retinal Vessel Structural Map Segmentation Through Grounding, Integrating and Adapting Multi-device, Multi-site, and Multi-modal Fundus Domains—0
COSMo: CLIP Talks on Open-Set Multi-Target Domain AdaptationCode0
Training-Free Model Merging for Multi-target Domain Adaptation—0
OurDB: Ouroboric Domain Bridging for Multi-Target Domain Adaptive Semantic Segmentation—0
ConvLoRA and AdaBN based Domain Adaptation via Self-TrainingCode2
Semantic Segmentation in Multiple Adverse Weather Conditions with Domain Knowledge Retention—0
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation—0
Strong-Weak Integrated Semi-supervision for Unsupervised Single and Multi Target Domain Adaptation—0
MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point CloudsCode0
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All datasetsOffice-31DomainNetOffice-HomeOBJ-MDA

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MCDAAccuracy89.6—Unverified
2DCGCTAccuracy88.8—Unverified
3MT-MTDAAccuracy85.2—Unverified
4AMEANAccuracy80.2—Unverified
5RevGradAccuracy73.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MCDAAccuracy34.5—Unverified
2DCGCTAccuracy34.4—Unverified
3MCCAccuracy28.8—Unverified
4DADAAccuracy21.5—Unverified
#ModelMetricClaimedVerifiedStatus
1MCDAAccuracy71.1—Unverified
2DCGCTAccuracy69.8—Unverified
3AMEANAccuracy64—Unverified
4RevGradAccuracy57.9—Unverified
#ModelMetricClaimedVerifiedStatus
1STMDA-RetinaNet[email protected]66.64—Unverified