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 63016350 of 6439 papers

TitleStatusHype
Domain adaptation for sequence labeling using hidden Markov models0
Reshaping Visual Datasets for Domain Adaptation0
Non-Linear Domain Adaptation with Boosting0
Domain Adaptation of Majority Votes via Perturbed Variation-based Label Transfer0
ML-Tuned Constraint Grammars0
Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation0
DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionCode0
Predicate Argument Structure Analysis using Partially Annotated Corpora0
An Online Algorithm for Learning over Constrained Latent Representations using Multiple Views0
Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging0
A Self-learning Template Approach for Recognizing Named Entities from Web Text0
A Common Case of Jekyll and Hyde: The Synergistic Effect of Using Divided Source Training Data for Feature Augmentation0
JoBimText Visualizer: A Graph-based Approach to Contextualizing Distributional Similarity0
Multi-Domain Adaptation for SMT Using Multi-Task Learning0
Learning Latent Word Representations for Domain Adaptation using Supervised Word Clustering0
Semi-Supervised Representation Learning for Cross-Lingual Text Classification0
Towards a Hybrid Rule-based and Statistical Arabic-French Machine Translation System0
Edit Distance: A New Data Selection Criterion for Domain Adaptation in SMT0
Revisiting the Old Kitchen Sink: Do we Need Sentiment Domain Adaptation?0
TwitIE: An Open-Source Information Extraction Pipeline for Microblog TextCode0
Towards Domain Adaptation for Parsing Web Data0
Domain Adaptation for Parsing0
Applications of Semantic Publishing0
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations0
Dramatically Reducing Training Data Size Through Vocabulary Saturation0
Shallow Semantically-Informed PBSMT and HPBSMT0
Improving MT System Using Extracted Parallel Fragments of Text from Comparable Corpora0
A Hybrid Word Alignment Model for Phrase-Based Statistical Machine Translation0
The RWTH Aachen Machine Translation System for WMT 20130
The TALP-UPC Phrase-Based Translation Systems for WMT13: System Combination with Morphology Generation, Domain Adaptation and Corpus Filtering0
Uses of Monolingual In-Domain Corpora for Cross-Domain Adaptation with Hybrid MT Approaches0
Edinburgh's Machine Translation Systems for European Language Pairs0
Unsupervised Linguistically-Driven Reliable Dependency Parses Detection and Self-Training for Adaptation to the Biomedical Domain0
GenNext: A Consolidated Domain Adaptable NLG System0
Topic Models + Word Alignment = A Flexible Framework for Extracting Bilingual Dictionary from Comparable Corpus0
Online Active Learning for Cost Sensitive Domain Adaptation0
A Learner Corpus-based Approach to Verb Suggestion for ESL0
Learning a Phrase-based Translation Model from Monolingual Data with Application to Domain Adaptation0
Vector Space Model for Adaptation in Statistical Machine Translation0
A Multi-Domain Translation Model Framework for Statistical Machine Translation0
Hierarchical Phrase Table Combination for Machine Translation0
Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction0
Dependency Parser Adaptation with Subtrees from Auto-Parsed Target Domain Data0
SenseSpotting: Never let your parallel data tie you to an old domain0
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation0
Fast and Adaptive Online Training of Feature-Rich Translation Models0
Class Proportion Estimation with Application to Multiclass Anomaly Rejection0
Automatically identifying implicit discourse relations using annotated data and raw corpora (Identification automatique des relations discursives « implicites » \`a partir de donn\'ees annot\'ees et de corpus bruts) [in French]0
Tagging Opinion Phrases and their Targets in User Generated Textual Reviews0
Using Other Learner Corpora in the 2013 NLI Shared Task0
Show:102550
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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