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 1201–1250 of 1951 papers

TitleStatusHype
Towards Adaptive Unknown Authentication for Universal Domain Adaptation by Classifier Paradox—0
Towards Discriminative Representation Learning for Unsupervised Person Re-identification—0
Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation—0
Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation—0
Towards Model Generalization for Monocular 3D Object Detection—0
Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation—0
Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation—0
Towards Realizing the Value of Labeled Target Samples: a Two-Stage Approach for Semi-Supervised Domain Adaptation—0
Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging—0
Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation—0
Towards Trustworthy Unsupervised Domain Adaptation: A Representation Learning Perspective for Enhancing Robustness, Discrimination, and Generalization—0
Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning—0
Towards Unsupervised Domain Adaptation via Domain-Transformer—0
Towards Reusable Network Components by Learning Compatible Representations—0
Transferable Deployment of Semantic Edge Inference Systems via Unsupervised Domain Adaption—0
Transferable Feature Learning on Graphs Across Visual Domains—0
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification—0
Transferable Query Selection for Active Domain Adaptation—0
Transfer Alignment Network for Double Blind Unsupervised Domain Adaptation—0
Transfering Low-Frequency Features for Domain Adaptation—0
Transfer Joint Matching for Unsupervised Domain Adaptation—0
Transferrable Operative Difficulty Assessment in Robot-assisted Teleoperation: A Domain Adaptation Approach—0
Transferrable Prototypical Networks for Unsupervised Domain Adaptation—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Trust And Balance: Few Trusted Samples Pseudo-Labeling and Temperature Scaled Loss for Effective Source-Free Unsupervised Domain Adaptation—0
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation—0
Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction—0
TWINs: Two Weighted Inconsistency-reduced Networks for Partial Domain Adaptation—0
Two-phase Pseudo Label Densification for Self-training based Domain Adaptation—0
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation—0
UMAD: Universal Model Adaptation under Domain and Category Shift—0
UMFN: Unified Multi-Domain Face Normalization for Joint Cross-domain Prototype Learning and Heterogeneous Face Recognition—0
Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation—0
Uncertainty-Aware Alignment Network for Cross-Domain Video-Text Retrieval—0
Uncertainty-Aware Alignment Network for Cross-Domain Video-Text Retrieval—0
Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation—0
Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection—0
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation—0
Uncertainty-Aware Pseudo Label Refinery for Domain Adaptive Semantic Segmentation—0
Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression—0
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation—0
Uncertainty Reduction for Model Adaptation in Semantic Segmentation—0
Understanding and Estimating the Adaptability of Domain-Invariant Representations—0
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective—0
Unified Principles For Multi-Source Transfer Learning Under Label Shifts—0
UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages—0
Universal Multi-Source Domain Adaptation—0
Universal Person Re-Identification—0
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision—0
Unsupervised Adaptation of Semantic Segmentation Models without Source Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CORE-ReIDmAP84.4—Unverified
2EvoADAmAP84.3—Unverified
3LF2mAP83.2—Unverified
4AWBmAP80.6—Unverified
5CCTSEmAP78.4—Unverified
6SpCLmAP76.7—Unverified
7MMTmAP71.2—Unverified
8SDAmAP70—Unverified
9AD-ClustermAP68.3—Unverified
10ECN++mAP63.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReIDmAP74.8—Unverified
2LF2mAP73.5—Unverified
3CCTSEmAP72.6—Unverified
4EvoADAmAP71.4—Unverified
5AWBmAP71—Unverified
6SpCLmAP68.8—Unverified
7MMTmAP65.1—Unverified
8SDAmAP61.4—Unverified
9SNRmAP58.1—Unverified
10ACTmAP54.5—Unverified
#ModelMetricClaimedVerifiedStatus
1ALDI++(Resnet50+FPN)[email protected]66.8—Unverified
2RT-DATR(640x640, real-time)[email protected]52.7—Unverified
3MRT[email protected]51.2—Unverified
4DDT[email protected]50—Unverified
5MIC[email protected]47.6—Unverified
6O2net[email protected]46.8—Unverified
7LGCL (supervised)[email protected]46.7—Unverified
8LGCL (unsupervised)[email protected]45.3—Unverified
9SAD[email protected]45.2—Unverified
10ALDI-DETR (ResNet-50, 800px)[email protected]44.8—Unverified
#ModelMetricClaimedVerifiedStatus
1FFTATAccuracy91.4—Unverified
2TransAdapter-BAccuracy89.4—Unverified
3SAMBAccuracy86.2—Unverified
4PDA (CLIP, ViT-B/16)Accuracy85.7—Unverified
5SSRT-BAccuracy85.43—Unverified
6EUDAAccuracy84.9—Unverified
7ProDeAccuracy84.5—Unverified
8ECB (CNN)Accuracy81.2—Unverified
9CDTransAccuracy80.5—Unverified
10JAN [cite:ICML17JAN]Accuracy76.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReID V2mAP44.1—Unverified
2CORE-ReIDmAP41.9—Unverified
3CORE-ReID V2 TinymAP35.8—Unverified
4CCTSEmAP33.2—Unverified
5UMDAmAP32.7—Unverified
6AWBmAP30.6—Unverified
7SpClmAP25.4—Unverified
8SDAmAP23.2—Unverified
9MMTmAP22.9—Unverified
10DG-Net++mAP22.1—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 (baseline), BatchNorm Adaptation, 8 samplesmean Corruption Error (mCE)65—Unverified
2ResNet50 (baseline), BatchNorm Adaptation, full adaptationmean Corruption Error (mCE)62.2—Unverified
3ResNet50 + ENTmean Corruption Error (mCE)51.6—Unverified
4ResNet50 + RPLmean Corruption Error (mCE)50.5—Unverified
5ResNet50+DeepAug+AugMix, BatchNorm Adaptation, 8 samplesmean Corruption Error (mCE)48.4—Unverified
6ResNet50+DeepAug+AugMix, BatchNorm Adaptation, full adaptationmean Corruption Error (mCE)45.4—Unverified
7ResNeXt101 32x8d + ENTmean Corruption Error (mCE)44.3—Unverified
8ResNeXt101 32x8d + RPLmean Corruption Error (mCE)43.2—Unverified
9ResNeXt101 32x8d + IG-3.5B + RPLmean Corruption Error (mCE)40.9—Unverified
10ResNeXt101 32x8d + IG-3.5B + ENTmean Corruption Error (mCE)40.8—Unverified
#ModelMetricClaimedVerifiedStatus
1MIC+CSImIoU (13 classes)75.9—Unverified
2DCFmIoU (13 classes)75.9—Unverified
3DIDAmIoU (13 classes)70.1—Unverified
4Sepico + HIASTmIoU (13 classes)68.1—Unverified
5CLUDA+HRDAmIoU67.2—Unverified
6SePiCo (DeepLabv2 ResNet-101)mIoU (13 classes)66.5—Unverified
7G2LmIoU (13 classes)64.4—Unverified
8FAFSmIoU (13 classes)61.4—Unverified
9DAFormer+CSImIoU61.4—Unverified
10AdaptSeg + HIASTmIoU (13 classes)60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReID V2mAP49.5—Unverified
2MATNet+DMDUmAP49.25—Unverified
3MGR-GCLmAP48.73—Unverified
4PLMmAP47.37—Unverified
5CSP+FCDmAP45.6—Unverified
6PALmAP42.04—Unverified
7CORE-ReID V2 TinymAP40.17—Unverified
8SPCLmAP38.9—Unverified
9UDARmAP35.8—Unverified
10MMTmAP35.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReIDmAP45.2—Unverified
2CCTSEmAP34.5—Unverified
3AWBmAP30.7—Unverified
4SpCLmAP26.5—Unverified
5SDAmAP25.6—Unverified
6MMTmAP23.3—Unverified
7MMCLmAP16.2—Unverified
8ECN++mAP16—Unverified
9SSGmAP13.3—Unverified
10ECNmAP10.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ALDI++[email protected]77.8—Unverified
2ALDI-YOLO[email protected]75—Unverified
3MIC(ALDI frame)[email protected]73.1—Unverified
4AT(ALDI frame)[email protected]72—Unverified
5SADA(ALDI frame)[email protected]71.8—Unverified
6PT(ALDI frame)[email protected]70.6—Unverified
7RT-DATR(real-time, 640x640)[email protected]67.2—Unverified
8DDT[email protected]64—Unverified
9MRT[email protected]62—Unverified
10MILA[email protected]57.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReID V2mAP57.99—Unverified
2CORE-ReID V2 TinymAP55.14—Unverified
3DMDUmAP53.97—Unverified
4UDARmAP52.9—Unverified
5MGR-GCLmAP47.59—Unverified
6PMLmAP46—Unverified
7PALmAP45.14—Unverified
8MLmAP45—Unverified
9VDAFR-143.69—Unverified
10CSP+FCDmAP42.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReID V2mAP63.02—Unverified
2CORE-ReID V2 TinymAP59.69—Unverified
3DMDUmAP56.73—Unverified
4UDARmAP55.3—Unverified
5MGR-GCLmAP50.56—Unverified
6PLMmAP49.41—Unverified
7MLmAP48.7—Unverified