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 48014850 of 6439 papers

TitleStatusHype
Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment0
Mixed Set Domain Adaptation0
Mixing Multiple Translation Models in Statistical Machine Translation0
MixNorm: Test-Time Adaptation Through Online Normalization Estimation0
MixStyle Neural Networks for Domain Generalization and Adaptation0
Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space Detection Approach0
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation0
MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation0
ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation0
ML-Optimization of Ported Constraint Grammars0
MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning0
ML-Tuned Constraint Grammars0
MM-PhyRLHF: Reinforcement Learning Framework for Multimodal Physics Question-Answering0
MobiFuse: Learning Universal Human Mobility Patterns through Cross-domain Data Fusion0
MoCap-to-Visual Domain Adaptation for Efficient Human Mesh Estimation from 2D Keypoints0
Modality-Collaborative Test-Time Adaptation for Action Recognition0
MoDA: Map style transfer for self-supervised Domain Adaptation of embodied agents0
MODA: Motion-Drift Augmentation for Inertial Human Motion Analysis0
Model Adaptation: Unsupervised Domain Adaptation without Source Data0
Model-Contrastive Federated Domain Adaptation0
Model-driven Simulations for Deep Convolutional Neural Networks0
Modeling Electrical Motor Dynamics using Encoder-Decoder with Recurrent Skip Connection0
Modeling non-standard language0
Modeling Skip-Grams for Event Detection with Convolutional Neural Networks0
Modeling Temporality of Human Intentions by Domain Adaptation0
Modelling Irony in Twitter0
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation0
MODfinity: Unsupervised Domain Adaptation with Multimodal Information Flow Intertwining0
ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization0
Modular Domain Adaptation0
Modular Domain Adaptation for Conformer-Based Streaming ASR0
Moment-Based Domain Adaptation: Learning Bounds and Algorithms0
MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models0
Monocular 3D Object Detection via Feature Domain Adaptation0
Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision0
Monocular pose estimation of articulated surgical instruments in open surgery0
MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain0
More diverse more adaptive: Comprehensive Multi-task Learning for Improved LLM Domain Adaptation in E-commerce0
More is Better: Deep Domain Adaptation with Multiple Sources0
More or less supervised supersense tagging of Twitter0
More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation0
Moses: Efficient Exploitation of Cross-device Transferable Features for Tensor Program Optimization0
Motion-Guided Masking for Spatiotemporal Representation Learning0
MOT: Masked Optimal Transport for Partial Domain Adaptation0
MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR0
MSDA: Monocular Self-supervised Domain Adaptation for 6D Object Pose Estimation0
MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation0
MSSDA: Multi-Sub-Source Adaptation for Diabetic Foot Neuropathy Recognition0
MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach For Indoor Localization0
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry0
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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