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 801850 of 1951 papers

TitleStatusHype
EndoUDA: A modality independent segmentation approach for endoscopy imaging0
Adversarial Domain Adaptation for Cell Segmentation0
Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks0
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations0
Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification0
Hashing in the Zero Shot Framework with Domain Adaptation0
EMTL: A Generative Domain Adaptation Approach0
Heterogeneous domain adaptation: An unsupervised approach0
Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation0
Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification0
LangDA: Building Context-Awareness via Language for Domain Adaptive Semantic Segmentation0
Hierarchical Instance Mixing across Domains in Aerial Segmentation0
Cross-lingual Adaptation for Recipe Retrieval with Mixup0
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection0
EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation0
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation0
High-order Neighborhoods Know More: HyperGraph Learning Meets Source-free Unsupervised Domain Adaptation0
High-resolution semantically-consistent image-to-image translation0
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation0
Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation0
Confidence Score Weighting Adaptation for Source-Free Unsupervised Domain Adaptation0
How does Contrastive Pre-training Connect Disparate Domains?0
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?0
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation0
Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation0
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id0
Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images0
Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation0
Learn by Guessing: Multi-Step Pseudo-Label Refinement for Person Re-Identification0
ICMSC: Intra- and Cross-modality Semantic Consistency for Unsupervised Domain Adaptation on Hip Joint Bone Segmentation0
EGFormer: Towards Efficient and Generalizable Multimodal Semantic Segmentation0
IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes0
Cryo-shift: Reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization0
Image-to-image domain adaptation for vehicle re-identification0
Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Air Quality Sensor Fusion0
Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment0
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation0
Confidence-Guided Unsupervised Domain Adaptation for Cerebellum Segmentation0
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation0
Confidence Calibration for Domain Generalization under Covariate Shift0
Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation0
Importance Weighted Adversarial Nets for Partial Domain Adaptation0
EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification0
A Brief Review of Domain Adaptation0
Curriculum Manager for Source Selection in Multi-Source Domain Adaptation0
Frustratingly Easy Uncertainty Estimation for Distribution Shift0
Edge-preserving Domain Adaptation for semantic segmentation of Medical Images0
Adapting Object Detectors with Conditional Domain Normalization0
Improving Domain Adaptation Through Class Aware Frequency Transformation0
Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection0
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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
#ModelMetricClaimedVerifiedStatus
1CORE-ReIDmAP74.8Unverified
2LF2mAP73.5Unverified
3CCTSEmAP72.6Unverified
4EvoADAmAP71.4Unverified
5AWBmAP71Unverified
6SpCLmAP68.8Unverified
7MMTmAP65.1Unverified
8SDAmAP61.4Unverified
9SNRmAP58.1Unverified
10ACTmAP54.5Unverified
#ModelMetricClaimedVerifiedStatus
1ALDI++(Resnet50+FPN)mAP@0.566.8Unverified
2RT-DATR(640x640, real-time)mAP@0.552.7Unverified
3MRTmAP@0.551.2Unverified
4DDTmAP@0.550Unverified
5MICmAP@0.547.6Unverified
6O2netmAP@0.546.8Unverified
7LGCL (supervised)mAP@0.546.7Unverified
8LGCL (unsupervised)mAP@0.545.3Unverified
9SADmAP@0.545.2Unverified
10AWADAmAP@0.544.8Unverified
#ModelMetricClaimedVerifiedStatus
1FFTATAccuracy91.4Unverified
2TransAdapter-BAccuracy89.4Unverified
3SAMBAccuracy86.2Unverified
4PDA (CLIP, ViT-B/16)Accuracy85.7Unverified
5SSRT-BAccuracy85.43Unverified
6EUDAAccuracy84.9Unverified
7ProDeAccuracy84.5Unverified
8ECB (CNN)Accuracy81.2Unverified
9CDTransAccuracy80.5Unverified
10JAN [cite:ICML17JAN]Accuracy76.8Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReID V2mAP44.1Unverified
2CORE-ReIDmAP41.9Unverified
3CORE-ReID V2 TinymAP35.8Unverified
4CCTSEmAP33.2Unverified
5UMDAmAP32.7Unverified
6AWBmAP30.6Unverified
7SpClmAP25.4Unverified
8SDAmAP23.2Unverified
9MMTmAP22.9Unverified
10DG-Net++mAP22.1Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 (baseline), BatchNorm Adaptation, 8 samplesmean Corruption Error (mCE)65Unverified
2ResNet50 (baseline), BatchNorm Adaptation, full adaptationmean Corruption Error (mCE)62.2Unverified
3ResNet50 + ENTmean Corruption Error (mCE)51.6Unverified
4ResNet50 + RPLmean Corruption Error (mCE)50.5Unverified
5ResNet50+DeepAug+AugMix, BatchNorm Adaptation, 8 samplesmean Corruption Error (mCE)48.4Unverified
6ResNet50+DeepAug+AugMix, BatchNorm Adaptation, full adaptationmean Corruption Error (mCE)45.4Unverified
7ResNeXt101 32x8d + ENTmean Corruption Error (mCE)44.3Unverified
8ResNeXt101 32x8d + RPLmean Corruption Error (mCE)43.2Unverified
9ResNeXt101 32x8d + IG-3.5B + RPLmean Corruption Error (mCE)40.9Unverified
10ResNeXt101 32x8d + IG-3.5B + ENTmean Corruption Error (mCE)40.8Unverified
#ModelMetricClaimedVerifiedStatus
1MIC+CSImIoU (13 classes)75.9Unverified
2DCFmIoU (13 classes)75.9Unverified
3DIDAmIoU (13 classes)70.1Unverified
4Sepico + HIASTmIoU (13 classes)68.1Unverified
5CLUDA+HRDAmIoU67.2Unverified
6SePiCo (DeepLabv2 ResNet-101)mIoU (13 classes)66.5Unverified
7G2LmIoU (13 classes)64.4Unverified
8FAFSmIoU (13 classes)61.4Unverified
9DAFormer+CSImIoU61.4Unverified
10AdaptSeg + HIASTmIoU (13 classes)60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReID V2mAP49.5Unverified
2MATNet+DMDUmAP49.25Unverified
3MGR-GCLmAP48.73Unverified
4PLMmAP47.37Unverified
5CSP+FCDmAP45.6Unverified
6PALmAP42.04Unverified
7CORE-ReID V2 TinymAP40.17Unverified
8SPCLmAP38.9Unverified
9UDARmAP35.8Unverified
10MMTmAP35.3Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReIDmAP45.2Unverified
2CCTSEmAP34.5Unverified
3AWBmAP30.7Unverified
4SpCLmAP26.5Unverified
5SDAmAP25.6Unverified
6MMTmAP23.3Unverified
7MMCLmAP16.2Unverified
8ECN++mAP16Unverified
9SSGmAP13.3Unverified
10ECNmAP10.2Unverified
#ModelMetricClaimedVerifiedStatus
1ALDI++mAP@0.577.8Unverified
2ALDI-YOLOmAP@0.575Unverified
3MIC(ALDI frame)mAP@0.573.1Unverified
4AT(ALDI frame)mAP@0.572Unverified
5SADA(ALDI frame)mAP@0.571.8Unverified
6PT(ALDI frame)mAP@0.570.6Unverified
7RT-DATR(real-time, 640x640)mAP@0.567.2Unverified
8DDTmAP@0.564Unverified
9MRTmAP@0.562Unverified
10MILAmAP@0.557.4Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReID V2mAP57.99Unverified
2CORE-ReID V2 TinymAP55.14Unverified
3DMDUmAP53.97Unverified
4UDARmAP52.9Unverified
5MGR-GCLmAP47.59Unverified
6PMLmAP46Unverified
7PALmAP45.14Unverified
8MLmAP45Unverified
9VDAFR-143.69Unverified
10CSP+FCDmAP42.7Unverified
#ModelMetricClaimedVerifiedStatus
1CORE-ReID V2mAP63.02Unverified
2CORE-ReID V2 TinymAP59.69Unverified
3DMDUmAP56.73Unverified
4UDARmAP55.3Unverified
5MGR-GCLmAP50.56Unverified
6PLMmAP49.41Unverified
7MLmAP48.7Unverified