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
Gibson Env: Real-World Perception for Embodied AgentsCode0
Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain AdaptationCode0
Generate To Adapt: Aligning Domains using Generative Adversarial NetworksCode0
Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience ReplayCode0
Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray FluoroscopyCode0
Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation With Implicit Neural RepresentationsCode0
Generalizing Across Domains via Cross-Gradient TrainingCode0
Generalizing A Person Retrieval Model Hetero- and HomogeneouslyCode0
Gradual Domain Adaptation without Indexed Intermediate DomainsCode0
Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical SamplesCode0
A Comparison of Strategies for Source-Free Domain AdaptationCode0
Continual Unsupervised Domain Adaptation for Semantic SegmentationCode0
Adversarial Invariant LearningCode0
Generalizable Image Repair for Robust Visual Autonomous RacingCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
General Frameworks for Conditional Two-Sample TestingCode0
Adversarial Feature Equilibrium Network for Multimodal Change Detection in Heterogeneous Remote Sensing ImagesCode0
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data SelectionCode0
Gaze360: Physically Unconstrained Gaze Estimation in the WildCode0
GCAL: Adapting Graph Models to Evolving Domain ShiftsCode0
Continually Improving Extractive QA via Human FeedbackCode0
Continual Learning of Neural Machine Translation within Low Forgetting Risk RegionsCode0
Adversarial Feature DesensitizationCode0
From Third Person to First Person: Dataset and Baselines for Synthesis and RetrievalCode0
A Study of Residual Adapters for Multi-Domain Neural Machine TranslationCode0
FusDom: Combining In-Domain and Out-of-Domain Knowledge for Continuous Self-Supervised LearningCode0
Continual Domain Adaptation on Aerial Images under Gradually Degrading WeatherCode0
From Priest to Doctor: Domain Adaptaion for Low-Resource Neural Machine TranslationCode0
Adversarial Feature Augmentation for Unsupervised Domain AdaptationCode0
Continual Coarse-to-Fine Domain Adaptation in Semantic SegmentationCode0
From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain AdaptationCode0
Continual BatchNorm Adaptation (CBNA) for Semantic SegmentationCode0
Contextual-Relation Consistent Domain Adaptation for Semantic SegmentationCode0
Contextual Parameter Generation for Universal Neural Machine TranslationCode0
Adversarial Feature Adaptation for Cross-lingual Relation ClassificationCode0
From Colors to Classes: Emergence of Concepts in Vision TransformersCode0
From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehaveCode0
A Strategy for Label Alignment in Deep Neural NetworksCode0
FreqAlign: Excavating Perception-oriented Transferability for Blind Image Quality Assessment from A Frequency PerspectiveCode0
FreSaDa: A French Satire Data Set for Cross-Domain Satire DetectionCode0
From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaptionCode0
Gradual Fine-Tuning for Low-Resource Domain AdaptationCode0
Improving Medical Report Generation with Adapter Tuning and Knowledge Enhancement in Vision-Language Foundation ModelsCode0
FogGuard: guarding YOLO against fog using perceptual lossCode0
Associative Domain AdaptationCode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain AdaptationCode0
Associative Alignment for Few-shot Image ClassificationCode0
Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand SegmentationCode0
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial ApproachCode0
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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