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 45514600 of 6439 papers

TitleStatusHype
Learning Agile Robotic Locomotion Skills by Imitating Animals0
Learning a Max-Margin Classifier for Cross-Domain Sentiment Analysis0
Learning and Deploying Robust Locomotion Policies with Minimal Dynamics Randomization0
Learning and Generalization with Mixture Data0
Learning an Invariant Hilbert Space for Domain Adaptation0
Learning a Phrase-based Translation Model from Monolingual Data with Application to Domain Adaptation0
Learning a POS tagger for AAVE-like language0
Learning-based Regularization for Cardiac Strain Analysis with Ability for Domain Adaptation0
Learning Bounds for Domain Adaptation0
On generalization in moment-based domain adaptation0
Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy0
Learning by Ignoring, with Application to Domain Adaptation0
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation0
Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions0
Learning causal representations for robust domain adaptation0
Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation0
Learning Compositional Representations for Effective Low-Shot Generalization0
Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation0
Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation0
Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation0
Learning Cross-domain Semantic-Visual Relationships for Transductive Zero-Shot Learning0
Learning Cross-modal Contrastive Features for Video Domain Adaptation0
Learning Deep Features for Shape Correspondence with Domain Invariance0
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach0
Learning Domain Adaptive Features with Unlabeled Domain Bridges0
Learning domain-invariant classifiers for infant cry sounds0
Learning Domain-Invariant Features for Out-of-Context News Detection0
Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples0
Learning Efficient Dialogue Policy from Demonstrations through Shaping0
Learning event representations for temporal segmentation of image sequences by dynamic graph embedding0
Learning Factorized Representations for Open-set Domain Adaptation0
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation0
Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts0
Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning0
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation0
Learning from few examples: Classifying sex from retinal images via deep learning0
Learning from Label Proportions and Covariate-shifted Instances0
Learning from Limited and Imperfect Data0
Learning from SAM: Harnessing a Foundation Model for Sim2Real Adaptation by Regularization0
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation0
Learning from Synthetic Data for Crowd Counting in the Wild0
Learning GANs and Ensembles Using Discrepancy0
Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation0
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models0
Learning Instance-Specific Adaptation for Cross-Domain Segmentation0
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds0
Learning Invariant Representation and Risk Minimized for Unsupervised Accent Domain Adaptation0
Learning Invariant Representations across Domains and Tasks0
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation0
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets0
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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