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 39013950 of 6439 papers

TitleStatusHype
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator0
Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation0
Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer0
TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks0
Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection0
Text Recognition in Real Scenarios with a Few Labeled Samples0
Texture Underfitting for Domain Adaptation0
Thai Financial Domain Adaptation of THaLLE -- Technical Report0
The ACCEPT Portal: An Online Framework for the Pre-editing and Post-editing of User-Generated Content0
The ADAPT Bilingual Document Alignment system at WMT160
The AFRL WMT19 Systems: Old Favorites and New Tricks0
The Art of the Steal: Purloining Deep Learning Models Developed for an Ultrasound Scanner to a Competitor Machine0
The Bare Necessities: Increasing Lexical Coverage for Multi-Word Domain Terms with Less Lexical Data0
The Bayesian Approach to Continual Learning: An Overview0
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error0
The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA0
The CORAL++ Algorithm for Unsupervised Domain Adaptation of Speaker Recogntion0
The Dark Side of the Language: Pre-trained Transformers in the DarkNet0
The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 20230
E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by a New Mahalanobis Distance Loss for Smart Computing0
The French Social Media Bank: a Treebank of Noisy User Generated Content0
The impact of near domain transfer on biomedical named entity recognition0
The IPN-CIC team system submission for the WMT 2020 similar language task0
The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 20170
The Last Mile to Supervised Performance: Semi-Supervised Domain Adaptation for Semantic Segmentation0
The LMU System for the CoNLL-SIGMORPHON 2017 Shared Task on Universal Morphological Reinflection0
The MLLP-UPV Spanish-Portuguese and Portuguese-Spanish Machine Translation Systems for WMT19 Similar Language Translation Task0
The MLLP-UPV Supervised Machine Translation Systems for WMT19 News Translation Task0
Theoretical Analysis of Domain Adaptation with Optimal Transport0
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data0
Inference via robust optimal transportation: theory and methods0
Theoretical Guarantees for Domain Adaptation with Hierarchical Optimal Transport0
On Deep Domain Adaptation: Some Theoretical Understandings0
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning0
The Over-Certainty Phenomenon in Modern UDA Algorithms0
The PASCAL Challenge on Grammar Induction0
The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge0
Thermal-Infrared Remote Target Detection System for Maritime Rescue based on Data Augmentation with 3D Synthetic Data0
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving0
The Role of Embedding Complexity in Domain-invariant Representations0
The Role of Linguistic Features in Domain Adaptation: TAG Parsing of Questions0
The Royalflush System for VoxCeleb Speaker Recognition Challenge 20220
The RWTH Aachen Machine Translation System for WMT 20130
The RWTH Aachen Machine Translation Systems for IWSLT 20170
The Samsung and University of Edinburgh’s submission to IWSLT170
The Sociolinguistic Foundations of Language Modeling0
The TALP-UPC Phrase-Based Translation Systems for WMT13: System Combination with Morphology Generation, Domain Adaptation and Corpus Filtering0
The TALP-UPC phrase-based translation systems for WMT12: Morphology simplification and domain adaptation0
The Trade-offs of Domain Adaptation for Neural Language Models0
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task0
Show:102550
← PrevPage 79 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