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 20512100 of 6439 papers

TitleStatusHype
Domain Adaptation of Synthetic Driving Datasets for Real-World Autonomous Driving0
UDApter -- Efficient Domain Adaptation Using AdaptersCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
RLSbench: Domain Adaptation Under Relaxed Label ShiftCode1
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain AdaptationCode1
Domain Re-Modulation for Few-Shot Generative Domain AdaptationCode1
Semi-Supervised Domain Adaptation with Source Label AdaptationCode1
Domain Adaptation via Rebalanced Sub-domain Alignment0
Interpretations of Domain Adaptations via Layer Variational AnalysisCode0
Class Overwhelms: Mutual Conditional Blended-Target Domain AdaptationCode0
Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction0
Efficient Domain Adaptation for Speech Foundation Models0
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation0
Open-Set Multi-Source Multi-Target Domain Adaptation0
Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains0
An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes0
Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases0
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets0
When Source-Free Domain Adaptation Meets Learning with Noisy Labels0
Lidar Upsampling with Sliced Wasserstein Distance0
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait RecognitionCode1
Learning Data Representations with Joint Diffusion ModelsCode1
Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic SegmentationCode1
Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain AdaptationCode0
Adaptive Machine Translation with Large Language ModelsCode1
N-Gram Nearest Neighbor Machine Translation0
DAFD: Domain Adaptation via Feature Disentanglement for Image Classification0
ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation0
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data0
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object DetectionCode1
Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning0
Preserving Fairness in AI under Domain Shift0
Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 20220
Decentralized Entropic Optimal Transport for Distributed Distribution Comparison0
Adversarial Learning Networks: Source-free Unsupervised Domain Incremental Learning0
Universal Domain Adaptation for Remote Sensing Image Scene ClassificationCode1
Discriminator-free Unsupervised Domain Adaptation for Multi-label Image ClassificationCode1
DEJA VU: Continual Model Generalization For Unseen DomainsCode1
Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object TrackingCode0
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach0
Low-Resource Compositional Semantic Parsing with Concept Pretraining0
ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition0
Leveraging Speaker Embeddings with Adversarial Multi-task Learning for Age Group Classification0
Robot Skill Learning Via Classical Robotics-Based Generated Datasets: Advantages, Disadvantages, and Future ImprovementCode0
Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation0
Domain Adaptation for Head Pose Estimation Using Relative Pose ConsistencyCode1
JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its ApplicationsCode0
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation SegmentationCode1
Vision Based Machine Learning Algorithms for Out-of-Distribution Generalisation0
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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