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 52015250 of 6439 papers

TitleStatusHype
LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-frame 3D Human Pose Estimation0
PoliTO-IIT-CINI Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition0
PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition0
Polylingual Tree-Based Topic Models for Translation Domain Adaptation0
Polymerized Feature-based Domain Adaptation for Cervical Cancer Dose Map Prediction0
PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation0
Population-aware Hierarchical Bayesian Domain Adaptation0
Population Expansion for Training Language Models with Private Federated Learning0
Population Matching Discrepancy and Applications in Deep Learning0
Pose-aware Adversarial Domain Adaptation for Personalized Facial Expression Recognition0
POS error detection in automatically annotated corpora0
Position: Epistemic Artificial Intelligence is Essential for Machine Learning Models to Know When They Do Not Know0
Positive-Unlabeled Domain Adaptation0
POS Tagging Experts via Topic Modeling0
POS-tagging of Historical Dutch0
Post-\'edition statistique pour l'adaptation aux domaines de sp\'ecialit\'e en traduction automatique (Statistical Post-Editing of Machine Translation for Domain Adaptation) [in French]0
POSTURE: Pose Guided Unsupervised Domain Adaptation for Human Body Part Segmentation0
Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability0
PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.0
Practical Imitation Learning in the Real World via Task Consistency Loss0
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks0
Taxonomy of Machine Learning Safety: A Survey and Primer0
Practical Parsing for Downstream Applications0
Predicate Argument Structure Analysis using Partially Annotated Corpora0
Predicting engagement in online social networks: Challenges and opportunities0
Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks0
Predicting the Future Behavior of a Time-Varying Probability Distribution0
Predicting the Success of Domain Adaptation in Text Similarity0
Predicting with Confidence on Unseen Distributions0
Predictive Optimization with Zero-Shot Domain Adaptation0
Pre-reordering for Statistical Machine Translation of Non-fictional Subtitles0
Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation0
Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-IdentificationCode0
SR-Stereo & DAPE: Stepwise Regression and Pre-trained Edges for Practical Stereo MatchingCode0
Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion MiningCode0
Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency RegularizationCode0
Prototypical Contrast Adaptation for Domain Adaptive Semantic SegmentationCode0
WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron MicroscopyCode0
Kneser-Ney Smoothing on Expected CountsCode0
Kernel Manifold AlignmentCode0
Prototypical Distillation and Debiased Tuning for Black-box Unsupervised Domain AdaptationCode0
K-Beam Minimax: Efficient Optimization for Deep Adversarial LearningCode0
Domain Adaptive Segmentation in Volume Electron Microscopy ImagingCode0
Knowledge Mining and Transferring for Domain Adaptive Object DetectionCode0
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial ApproachCode0
KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentationCode0
Adversarial Feature DesensitizationCode0
Judicious Selection of Training Data in Assisting Language for Multilingual Neural NERCode0
Known-class Aware Self-ensemble for Open Set Domain AdaptationCode0
Unsupervised Online Continual Learning for Automatic Speech RecognitionCode0
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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