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 41014150 of 6439 papers

TitleStatusHype
Confidence Estimation via Auxiliary Models0
Exploring Facial Expressions and Affective Domains for Parkinson Detection0
Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization0
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks0
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation0
Multi-Model Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation0
A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place RecognitionCode1
Two-phase Pseudo Label Densification for Self-training based Domain Adaptation0
Unsupervised Adversarial Domain Adaptation For Barrett's Segmentation0
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition0
COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNNCode0
CrossNER: Evaluating Cross-Domain Named Entity RecognitionCode1
Scale Aware Adaptation for Land-Cover Classification in Remote Sensing ImageryCode1
UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identificationCode1
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation0
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution RobustnessCode0
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep ClusteringCode1
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation0
Reprogramming Language Models for Molecular Representation Learning0
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation0
Data-Efficient Methods for Dialogue Systems0
Batch Group Normalization0
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation0
Domain Adaptation with Incomplete Target Domains0
Domain Adaptation on Semantic Segmentation for Aerial Images0
Speaker Recognition Based on Deep Learning: An Overview0
DA^3:Dynamic Additive Attention Adaption for Memory-EfficientOn-Device Multi-Domain Learning0
Unsupervised Neural Domain Adaptation for Document Image Binarization0
Aligning Hyperbolic Representations: an Optimal Transport-based approachCode0
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training0
Domain Adaptation of NMT models for English-Hindi Machine Translation Task : AdapMT Shared Task ICON 20200
AdapNMT : Neural Machine Translation with Technical Domain Adaptation for Indic Languages0
MUCS@Adap-MT 2020: Low Resource Domain Adaptation for Indic Machine Translation0
Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task0
Benchmarking Automated Review Response Generation for the Hospitality Domain0
InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research0
Unsupervised Approach for Zero-Shot Experiments: Bhojpuri–Hindi and Magahi–Hindi@LoResMT 20200
Findings of the LoResMT 2020 Shared Task on Zero-Shot for Low-Resource languages0
Filtering Back-Translated Data in Unsupervised Neural Machine Translation0
Effective Use of Target-side Context for Neural Machine Translation0
Intermediate Self-supervised Learning for Machine Translation Quality Estimation0
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations0
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction0
Data Augmentation with norm-VAE for Unsupervised Domain AdaptationCode1
Sim2Real for Self-Supervised Monocular Depth and Segmentation0
Learning to Adapt to Evolving DomainsCode1
Adapting Neural Architectures Between DomainsCode0
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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