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 58515900 of 6439 papers

TitleStatusHype
Few-shot Image Generation with Diffusion ModelsCode0
Deep Defocus Map Estimation Using Domain AdaptationCode0
Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-IdentificationCode0
Few-shot Hybrid Domain Adaptation of Image GeneratorsCode0
Weakly-supervised Caricature Face Parsing through Domain AdaptationCode0
Asymmetric Tri-training for Unsupervised Domain AdaptationCode0
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category ShiftCode0
De-Confusing Pseudo-Labels in Source-Free Domain AdaptationCode0
Few-shot Fine-tuning is All You Need for Source-free Domain AdaptationCode0
DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionCode0
Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLPCode0
Multinomial Adversarial Networks for Multi-Domain Text ClassificationCode0
Self-supervised Domain Adaptation for Computer Vision TasksCode0
Camera Alignment and Weighted Contrastive Learning for Domain Adaptation in Video Person ReIDCode0
Calibration of Network Confidence for Unsupervised Domain Adaptation Using Estimated AccuracyCode0
Universal Domain Adaptation for Semantic SegmentationCode0
Asymmetric Co-Training for Source-Free Few-Shot Domain AdaptationCode0
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State TrackingCode0
Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain AdaptationCode0
DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image SegmentationCode0
Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace SelectionCode0
A Study of Residual Adapters for Multi-Domain Neural Machine TranslationCode0
1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection TrackCode0
Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired ClassifiersCode0
Multiple Source Domain Adaptation with Adversarial Training of Neural NetworksCode0
DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer LearningCode0
A Strategy for Label Alignment in Deep Neural NetworksCode0
Multi-Prompt Alignment for Multi-Source Unsupervised Domain AdaptationCode0
DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task InteractionsCode0
Unsupervised Domain Adaptation with Progressive Adaptation of SubspacesCode0
Few-Shot Adaptation of Pre-Trained Networks for Domain ShiftCode0
FewRel 2.0: Towards More Challenging Few-Shot Relation ClassificationCode0
Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point AnnotationCode0
Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class BiasCode0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Self-Supervised Multi-Category Counting Networks for Automatic Check-OutCode0
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein EstimatorCode0
There Are Many Consistent Explanations of Unlabeled Data: Why You Should AverageCode0
DATE: Domain Adaptive Product Seeker for E-commerceCode0
Principled Federated Domain Adaptation: Gradient Projection and Auto-WeightingCode0
The Representation Jensen-Shannon DivergenceCode0
U-vectors: Generating clusterable speaker embedding from unlabeled dataCode0
Feature-Critic Networks for Heterogeneous Domain GeneralizationCode0
Multi-source Distilling Domain AdaptationCode0
Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019Code0
Feature Distribution Matching for Federated Domain GeneralizationCode0
Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman ProcessCode0
Data Valuation using Reinforcement LearningCode0
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-TrainingCode0
Multi-source Domain Adaptation for Panoramic Semantic SegmentationCode0
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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