SOTAVerified

Unsupervised Domain Adaptation

Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.

Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation

Papers

Showing 110 of 1951 papers

TitleStatusHype
CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble FusionCode0
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud RecognitionCode0
Unlocking Constraints: Source-Free Occlusion-Aware Seamless SegmentationCode0
Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments0
MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification0
Customizing Speech Recognition Model with Large Language Model Feedback0
Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object DetectorCode1
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image SegmentationCode0
Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI0
MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CORE-ReIDmAP84.4Unverified
2EvoADAmAP84.3Unverified
3LF2mAP83.2Unverified
4AWBmAP80.6Unverified
5CCTSEmAP78.4Unverified
6SpCLmAP76.7Unverified
7MMTmAP71.2Unverified
8SDAmAP70Unverified
9AD-ClustermAP68.3Unverified
10ECN++mAP63.8Unverified