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 60016050 of 6439 papers

TitleStatusHype
Domain Adaptation with Soft-margin multiple feature-kernel learning beats Deep Learning for surveillance face recognition0
Neural Structural Correspondence Learning for Domain AdaptationCode0
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles0
Kernel Selection using Multiple Kernel Learning and Domain Adaptation in Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance Scenario0
Multi-domain Adaptation for Statistical Machine Translation Based on Feature Augmentation0
Adding syntactic structure to bilingual terminology for improved domain adaptation0
Dialogue manager domain adaptation using Gaussian process reinforcement learning0
Hard Negative Mining for Metric Learning Based Zero-Shot Classification0
Leveraging over intact priors for boosting control and dexterity of prosthetic hands by amputees0
Domain Separation NetworksCode0
Multi-task Domain Adaptation for Sequence Tagging0
Play and Learn: Using Video Games to Train Computer Vision Models0
Dictionary-based Domain Adaptation of MT Systems without Retraining0
PJAIT Systems for the WMT 20160
Bi-Transferring Deep Neural Networks for Domain Adaptation0
Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning0
JU-USAAR: A Domain Adaptive MT System0
A Domain Adaptation Regularization for Denoising Autoencoders0
Deep multi-task learning with low level tasks supervised at lower layers0
A Two-stage Approach for Extending Event Detection to New Types via Neural Networks0
Sentiment Domain Adaptation with Multiple Sources0
Data Selection for IT Texts using Paragraph Vector0
The ADAPT Bilingual Document Alignment system at WMT160
Semi-supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In-domain Data0
Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams0
Transductive Adaptation of Black Box Predictions0
Extract Domain-specific Paraphrase from Monolingual Corpus for Automatic Evaluation of Machine Translation0
Generating Disambiguating Paraphrases for Structurally Ambiguous Sentences0
Different Flavors of GUM: Evaluating Genre and Sentence Type Effects on Multilayer Corpus Annotation Quality0
On Regularization Parameter Estimation under Covariate ShiftCode0
Learning the Roots of Visual Domain Shift0
Deep Reconstruction-Classification Networks for Unsupervised Domain AdaptationCode0
Guided Alignment Training for Topic-Aware Neural Machine TranslationCode0
Autoapprentissage pour le regroupement en locuteurs : premi\`eres investigations (First investigations on self trained speaker diarization )0
Domain Adaptation for Neural Networks by Parameter Augmentation0
Coupled Generative Adversarial NetworksCode0
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation0
Active Long Term Memory Networks0
Multivariate Regression on the Grassmannian for Predicting Novel Domains0
When Annotation Schemes Change Rules Help: A Configurable Approach to Coreference Resolution beyond OntoNotes0
Learning a POS tagger for AAVE-like language0
Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation0
When Naive Bayes Nearest Neighbors Meet Convolutional Neural Networks0
Syntactic Parsing of Web Queries with Question Intent0
Using Related Languages to Enhance Statistical Language Models0
Weakly Supervised Object Localization With Progressive Domain Adaptation0
An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation0
Domain Adaptation of Polarity Lexicon combining Term Frequency and Bootstrapping0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning0
VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval0
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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