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 40514100 of 6439 papers

TitleStatusHype
5W1H Extraction With Large Language Models0
70B-parameter large language models in Japanese medical question-answering0
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation0
Abductive Reasoning as Self-Supervision for Common Sense Question Answering0
A Brief Review of Domain Adaptation0
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning0
Accurate Unsupervised Joint Named-Entity Extraction from Unaligned Parallel Text0
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation0
A class alignment method based on graph convolution neural network for bearing fault diagnosis in presence of missing data and changing working conditions0
A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation0
AC-Mix: Self-Supervised Adaptation for Low-Resource Automatic Speech Recognition using Agnostic Contrastive Mixup0
A Common Case of Jekyll and Hyde: The Synergistic Effect of Using Divided Source Training Data for Feature Augmentation0
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation0
A comparative analysis of embedding models for patent similarity0
Supervised domain adaptation for building extraction from off-nadir aerial images0
A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping0
A Comparison of Sentence-Weighting Techniques for NMT0
A Comparison of Strategies for Source-Free Domain Adaptation0
Towards Understanding Domain Adapted Sentence Embeddings for Document Retrieval0
A Comprehensive Framework for Semantic Similarity Analysis of Human and AI-Generated Text Using Transformer Architectures and Ensemble Techniques0
A Comprehensive Survey on Source-free Domain Adaptation0
A contribution to Optimal Transport on incomparable spaces0
A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mapping0
A cross-domain recommender system using deep coupled autoencoders0
A Cross-Level Information Transmission Network for Predicting Phenotype from New Genotype: Application to Cancer Precision Medicine0
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition0
Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition0
Actions and Objects Pathways for Domain Adaptation in Video Question Answering0
Action Segmentation with Mixed Temporal Domain Adaptation0
Active Adversarial Domain Adaptation0
Active Domain Adaptation with False Negative Prediction for Object Detection0
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation0
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images0
Active Learning for Post-Editing Based Incrementally Retrained MT0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation0
Active Learning with Transfer Learning0
Active Long Term Memory Networks0
Actively Seeking and Learning from Live Data0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification0
Active Online Learning with Hidden Shifting Domains0
Active Online Domain Adaptation0
Active Semantic Localization with Graph Neural Embedding0
Active Sentiment Domain Adaptation0
Active Source Free Domain Adaptation0
Active Universal Domain Adaptation0
An Empirical Study on Activity Recognition in Long Surgical Videos0
ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training0
A Cycle GAN Approach for Heterogeneous Domain Adaptation in Land Use Classification0
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation0
AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space0
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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