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 58015850 of 6439 papers

TitleStatusHype
Effective Domain Mixing for Neural Machine Translation0
Human Centered NLP with User-Factor Adaptation0
Instance Weighting for Neural Machine Translation Domain AdaptationCode0
A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output0
Word-Context Character Embeddings for Chinese Word Segmentation0
Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain AdaptationCode0
Adaptive SVM+: Learning with Privileged Information for Domain Adaptation0
Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVMCode0
Large-Scale Domain Adaptation via Teacher-Student Learning0
WebVision Database: Visual Learning and Understanding from Web Data0
Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNNCode0
GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images0
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos0
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task0
Associative Domain AdaptationCode0
Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation0
Cost Weighting for Neural Machine Translation Domain Adaptation0
Unsupervised Domain Adaptation for Clinical Negation Detection0
The LMU System for the CoNLL-SIGMORPHON 2017 Shared Task on Universal Morphological Reinflection0
Multilingual Semantic Parsing And Code-SwitchingCode0
Feature Selection as Causal Inference: Experiments with Text Classification0
Adversarial Training for Cross-Domain Universal Dependency Parsing0
Cross-language Learning with Adversarial Neural Networks0
LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives0
Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes0
NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation0
XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model0
SemEval-2017 Task 12: Clinical TempEval0
KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical RecordsCode0
GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction0
Regularization techniques for fine-tuning in neural machine translation0
Curriculum Domain Adaptation for Semantic Segmentation of Urban ScenesCode0
DARLA: Improving Zero-Shot Transfer in Reinforcement LearningCode0
A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization0
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional DistributionsCode0
When Unsupervised Domain Adaptation Meets Tensor RepresentationsCode0
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation0
Domain Adaptation for Resume Classification Using Convolutional Neural Networks0
PAC-Bayes and Domain Adaptation0
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL0
Deep Domain Adaptation by Geodesic Distance Minimization0
Stable Distribution Alignment Using the Dual of the Adversarial Distance0
Zero-Shot Deep Domain Adaptation0
Wasserstein Distance Guided Representation Learning for Domain AdaptationCode0
Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics0
Zero-Shot Classification With Discriminative Semantic Representation Learning0
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation0
Parser Adaptation for Social Media by Integrating Normalization0
Sentence Embedding for Neural Machine Translation Domain Adaptation0
Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings0
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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