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

Showing 21512200 of 6439 papers

TitleStatusHype
Generalizable Image Repair for Robust Visual Autonomous RacingCode0
Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical SamplesCode0
Generalizing A Person Retrieval Model Hetero- and HomogeneouslyCode0
General Frameworks for Conditional Two-Sample TestingCode0
ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI SegmentationCode0
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data SelectionCode0
Con^2DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature RepresentationsCode0
Gaze360: Physically Unconstrained Gaze Estimation in the WildCode0
d-SNE: Domain Adaptation using Stochastic Neighborhood EmbeddingCode0
d-SNE: Domain Adaptation Using Stochastic Neighborhood EmbeddingCode0
Computational Imaging for Machine Perception: Transferring Semantic Segmentation beyond AberrationsCode0
From Third Person to First Person: Dataset and Baselines for Synthesis and RetrievalCode0
DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed RadiographsCode0
FusDom: Combining In-Domain and Out-of-Domain Knowledge for Continuous Self-Supervised LearningCode0
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain AdaptationCode0
FreqAlign: Excavating Perception-oriented Transferability for Blind Image Quality Assessment from A Frequency PerspectiveCode0
FreSaDa: A French Satire Data Set for Cross-Domain Satire DetectionCode0
FREDOM: Fairness Domain Adaptation Approach to Semantic Scene UnderstandingCode0
Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand SegmentationCode0
Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading ComprehensionCode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
From Colors to Classes: Emergence of Concepts in Vision TransformersCode0
DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language ModelsCode0
A Robust Learning Approach to Domain Adaptive Object DetectionCode0
FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation ModelsCode0
Fisher Deep Domain AdaptationCode0
Adversarial Distribution Balancing for Counterfactual ReasoningCode0
First U-Net Layers Contain More Domain Specific Information Than The Last OnesCode0
FogGuard: guarding YOLO against fog using perceptual lossCode0
From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehaveCode0
Few-shot Hybrid Domain Adaptation of Image GeneratorsCode0
Few-shot Image Generation with Diffusion ModelsCode0
Domain Transformer: Predicting Samples of Unseen, Future DomainsCode0
Comparison of single and multitask learning for predicting cognitive decline based on MRI dataCode0
Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace SelectionCode0
Few-shot Fine-tuning is All You Need for Source-free Domain AdaptationCode0
Comparing Machine Learning Techniques for Alfalfa Biomass Yield PredictionCode0
GCAL: Adapting Graph Models to Evolving Domain ShiftsCode0
A Compact Pretraining Approach for Neural Language ModelsCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
Few-Shot Adaptation of Pre-Trained Networks for Domain ShiftCode0
Adversarial Diffusion Model for Unsupervised Domain-Adaptive Semantic SegmentationCode0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Domain-Specific Batch Normalization for Unsupervised Domain AdaptationCode0
COMET: Contrastive Mean Teacher for Online Source-Free Universal Domain AdaptationCode0
FewRel 2.0: Towards More Challenging Few-Shot Relation ClassificationCode0
From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaptionCode0
Domain-shift adaptation via linear transformationsCode0
Domain Separation NetworksCode0
Feature-Critic Networks for Heterogeneous Domain GeneralizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FFTATAverage Accuracy96Unverified
2PMTransAverage Accuracy95.3Unverified
3CMKDAverage Accuracy94.4Unverified
4SSRT-B (ours)Average Accuracy93.5Unverified
5CDTransAverage Accuracy92.6Unverified
6CoViAverage Accuracy91.8Unverified
7GSDEAverage Accuracy91.7Unverified
8FixBiAverage Accuracy91.4Unverified
9Contrastive Adaptation NetworkAverage Accuracy90.6Unverified
10BIWAAAverage Accuracy90.5Unverified
#ModelMetricClaimedVerifiedStatus
1HALOmIoU78.1Unverified
2ILM-ASSLmIoU76.6Unverified
3DCFmIoU69.3Unverified
4HRDA+PiPamIoU68.2Unverified
5MICmIoU67.3Unverified
6FREDOM - TransformermIoU67Unverified
7HRDAmIoU65.8Unverified
8SePiComIoU64.3Unverified
9MIC + Guidance TrainingmIoU63.8Unverified
10DAFormer + ProCSTmIoU61.6Unverified
#ModelMetricClaimedVerifiedStatus
1HALOmIoU77.8Unverified
2DCFmIoU77.7Unverified
3ILM-ASSLmIoU76.1Unverified
4MICmIoU75.9Unverified
5HRDA+PiPamIoU75.6Unverified
6HRDAmIoU73.8Unverified
7FREDOM - TransformermIoU73.6Unverified
8HALOmIoU73.3Unverified
9SePiComIoU70.3Unverified
10DAFormer + ProCSTmIoU69.4Unverified
#ModelMetricClaimedVerifiedStatus
1SWGAccuracy92.3Unverified
2RCLAccuracy90Unverified
3PGA (ViT-L/14)Accuracy89.4Unverified
4PMTransAccuracy89Unverified
5CMKDAccuracy89Unverified
6MICAccuracy86.2Unverified
7PGA (ViT-B/16)Accuracy85.1Unverified
8ELSAccuracy84.6Unverified
9SDAT (ViT-B/16)Accuracy84.3Unverified
10CDTrans (DeiT-B)Accuracy80.5Unverified
#ModelMetricClaimedVerifiedStatus
1FFTATAccuracy93.8Unverified
2RCLAccuracy93.2Unverified
3MICAccuracy92.8Unverified
4SWGAccuracy92.7Unverified
5CMKDAccuracy91.8Unverified
6DePTAccuracy90.7Unverified
7SDAT(ViT)Accuracy89.8Unverified
8SFDA2++Accuracy89.6Unverified
9PMtransAccuracy88.8Unverified
10CoViAccuracy88.5Unverified
#ModelMetricClaimedVerifiedStatus
1CMKDAccuracy94.3Unverified
2MCC+NWDAccuracy90.7Unverified
3GLOT-DRAccuracy90.4Unverified
4SPLAccuracy90.3Unverified
5DFA-SAFNAccuracy90.2Unverified
6DADAAccuracy89.3Unverified
7DFA-ENTAccuracy89.1Unverified
8MEDMAccuracy88.9Unverified
9DDAAccuracy88.9Unverified
10IAFN+ENTAccuracy88.9Unverified
#ModelMetricClaimedVerifiedStatus
1SoRAmIoU78.8Unverified
2ReinmIoU77.6Unverified
3CoDAmIoU72.6Unverified
4Refign (HRDA)mIoU72.1Unverified
5HALOmIoU71.9Unverified
6MICmIoU70.4Unverified
7HRDAmIoU68Unverified
8Refign (DAFormer)mIoU65.5Unverified
9VBLC (DAFormer)mIoU64.2Unverified
10CMFormermIoU60.1Unverified
#ModelMetricClaimedVerifiedStatus
1FACTAccuracy98.8Unverified
2FAMCDAccuracy98.72Unverified
3DFA-MCDAccuracy98.6Unverified
4Mean teacherAccuracy98.26Unverified
5DRANetAccuracy98.2Unverified
6SHOTAccuracy98Unverified
7DFA-ENTAccuracy97.9Unverified
8CyCleGAN (Light-weight Calibrator)Accuracy97.1Unverified