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 60516100 of 6439 papers

TitleStatusHype
Going Deeper into Action Recognition: A Survey0
Learning Representations for Counterfactual InferenceCode0
Domain Adaptation in MT Using Titles in Wikipedia as a Parallel Corpus: Resources and Evaluation0
POS-tagging of Historical Dutch0
Using BabelNet to Improve OOV Coverage in SMT0
Domain Adaptation for Named Entity Recognition Using CRFs0
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis0
Synthesizing Training Images for Boosting Human 3D Pose Estimation0
Online Updating of Word Representations for Part-of-Speech Tagging0
Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding0
Adapting Models to Signal Degradation using Distillation0
Unsupervised Domain Adaptation in the Wild: Dealing with Asymmetric Label Sets0
How useful is photo-realistic rendering for visual learning?0
Lightweight Unsupervised Domain Adaptation by Convolutional Filter Reconstruction0
Beyond Sharing Weights for Deep Domain Adaptation0
DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout0
Revisiting Batch Normalization For Practical Domain AdaptationCode0
Learning Domain-Invariant Subspace using Domain Features and Independence MaximizationCode0
Part-of-Speech Tagging for Historical English0
Multi-domain Neural Network Language Generation for Spoken Dialogue Systems0
Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages0
Unsupervised Domain Adaptation Using Approximate Label Matching0
Unsupervised Domain Adaptation with Residual Transfer NetworksCode0
Unsupervised Transductive Domain Adaptation0
Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation0
Studying Very Low Resolution Recognition Using Deep Networks0
Adapting to All Domains at Once: Rewarding Domain Invariance in SMTCode0
Domain Adaptation and Transfer Learning in StochasticNets0
Feature-Level Domain Adaptation0
Scalable domain adaptation of convolutional neural networks0
PJAIT Systems for the IWSLT 2015 Evaluation Campaign Enhanced by Comparable Corpora0
What can we learn about CNNs from a large scale controlled object dataset?0
Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment0
Multi-View Domain Generalization for Visual Recognition0
Unsupervised Domain Adaptation With Imbalanced Cross-Domain Data0
Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation0
Unsupervised Domain Adaptation for Zero-Shot Learning0
Video Tracking Using Learned Hierarchical Features0
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints0
Towards Principled Unsupervised Learning0
Return of Frustratingly Easy Domain AdaptationCode0
When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks0
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization0
Simultaneous Deep Transfer Across Domains and TasksCode0
Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification0
Improving the Performance of an Example-Based Machine Translation System Using a Domain-specific Bilingual Lexicon0
Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation0
Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder0
Toshiba MT System Description for the WAT2015 Workshop0
Selecting Relevant Web Trained Concepts for Automated Event Retrieval0
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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