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 44014450 of 6439 papers

TitleStatusHype
Shuffle and Attend: Video Domain Adaptation0
Learning to See in the Dark with Events0
Multiple Class Novelty Detection Under Data Distribution Shift0
Learning to Detect Open Classes for Universal Domain AdaptationCode1
Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification0
Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation0
YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models -0
Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal ImageryCode1
Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification0
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification0
Adversarial Bipartite Graph Learning for Video Domain AdaptationCode1
Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening0
Domain Adaptive Semantic Segmentation Using Weak Labels0
Beyond H-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence0
Pixel-wise Crowd Understanding via Synthetic Data0
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data0
Few shot domain adaptation for in situ macromolecule structural classification in cryo-electron tomograms0
Unsupervised Disentanglement GAN for Domain Adaptive Person Re-Identification0
COVID-19 therapy target discovery with context-aware literature mining0
What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector0
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain AdaptationCode0
S^3Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data0
Learning from Scale-Invariant Examples for Domain Adaptation in Semantic SegmentationCode0
Siamese x-vector reconstruction for domain adapted speaker recognition0
Symmetric Positive Semi-definite Riemannian Geometry with Application to Domain Adaptation0
Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identificationCode1
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation0
Gradient Regularized Contrastive Learning for Continual Domain Adaptation0
Deep Co-Training with Task Decomposition for Semi-Supervised Domain AdaptationCode1
Self-Supervised Learning Across Domains0
On the Effectiveness of Image Rotation for Open Set Domain AdaptationCode1
MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint0
Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation0
Unsupervised Domain Adaptation in the Absence of Source Data0
Joint Disentangling and Adaptation for Cross-Domain Person Re-IdentificationCode1
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data0
DWMD: Dimensional Weighted Orderwise Moment Discrepancy for Domain-specific Hidden Representation Matching0
Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain AdaptationCode1
Unsupervised Domain Attention Adaptation Network for Caricature Attribute RecognitionCode1
PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentationCode1
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic SegmentationCode1
Domain2Vec: Domain Embedding for Unsupervised Domain AdaptationCode0
Learning to Match Distributions for Domain Adaptation0
Learning to Combine: Knowledge Aggregation for Multi-Source Domain AdaptationCode1
DACS: Domain Adaptation via Cross-domain Mixed SamplingCode1
Transferable Calibration with Lower Bias and Variance in Domain Adaptation0
Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds0
Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain AdaptationCode1
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry0
Unsupervised Multi-Target Domain Adaptation Through Knowledge DistillationCode1
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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