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 23012350 of 6439 papers

TitleStatusHype
EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse PrinciplesCode0
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the WildCode0
Few-shot Hybrid Domain Adaptation of Image GeneratorsCode0
Few-shot Image Generation with Diffusion ModelsCode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
A Curriculum-style Self-training Approach for Source-Free Semantic SegmentationCode0
Few-Shot Adaptation of Pre-Trained Networks for Domain ShiftCode0
FewRel 2.0: Towards More Challenging Few-Shot Relation ClassificationCode0
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein EstimatorCode0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Feature-Critic Networks for Heterogeneous Domain GeneralizationCode0
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-TrainingCode0
Contextual Parameter Generation for Universal Neural Machine TranslationCode0
Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVMCode0
Feather-Light Fourier Domain Adaptation in Magnetic Resonance ImagingCode0
Domain Differential Adaptation for Neural Machine TranslationCode0
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine TranslationCode0
MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point CloudsCode0
FAtNet: Cost-Effective Approach Towards Mitigating the Linguistic Bias in Speaker Verification SystemsCode0
Principled Federated Domain Adaptation: Gradient Projection and Auto-WeightingCode0
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum LearningCode0
Adversarial Feature Augmentation for Unsupervised Domain AdaptationCode0
Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace SelectionCode0
Domain Consensus Clustering for Universal Domain AdaptationCode0
Collaborative and Adversarial Network for Unsupervised Domain AdaptationCode0
FADE: Forecasting for Anomaly Detection on ECGCode0
A Study of Residual Adapters for Multi-Domain Neural Machine TranslationCode0
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware PerspectiveCode0
Domain-Conditioned Transformer for Fully Test-time AdaptationCode0
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional AdaptationCode0
Domain Conditional Predictors for Domain AdaptationCode0
Rethinking the Role of Pre-Trained Networks in Source-Free Domain AdaptationCode0
Fairness meets Cross-Domain Learning: a new perspective on Models and MetricsCode0
LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and ClassificationCode0
FAA-CLIP: Federated Adversarial Adaptation of CLIPCode0
Domain Borders Are There to Be Crossed With Federated Few-Shot AdaptationCode0
Extracting Relationships by Multi-Domain MatchingCode0
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual PersistenceCode0
Fact Checking Beyond Training SetCode0
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain AdaptationCode0
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving RegularizationCode0
CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost MicroscopesCode0
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated ExamplesCode0
FACT: Federated Adversarial Cross TrainingCode0
Exploring Adversarially Robust Training for Unsupervised Domain AdaptationCode0
Approximating a Target Distribution using Weight QueriesCode0
Domain Alignment Meets Fully Test-Time AdaptationCode0
Exploiting Semantic Localization in Highly Dynamic Wireless Networks Using Deep Homoscedastic Domain AdaptationCode0
Enhancing Autonomous Vehicle Perception in Adverse Weather through Image Augmentation during Semantic Segmentation TrainingCode0
Domain Agnostic Real-Valued Specificity PredictionCode0
Show:102550
← PrevPage 47 of 129Next →

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