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 46014650 of 6439 papers

TitleStatusHype
Learning Invariant Representations on Multilingual Language Models for Unsupervised Cross-Lingual Transfer0
Learning Latent Word Representations for Domain Adaptation using Supervised Word Clustering0
Learning List-Level Domain-Invariant Representations for Ranking0
Learning New Tricks from Old Dogs -- Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment0
Learning Numerical Observers using Unsupervised Domain Adaptation0
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images0
Learning Optimal Policies from Observational Data0
Learning Part Segmentation from Synthetic Animals0
Learning Robust Models by Countering Spurious Correlations0
Learning Sampling Policies for Domain Adaptation0
Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification0
Learning structure-from-motion from motion0
Learning Target Domain Specific Classifier for Partial Domain Adaptation0
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation0
Learning the Roots of Visual Domain Shift0
Learning Timeline Difference for Text Categorization0
Learning to Adapt Invariance in Memory for Person Re-identification0
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation0
Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation0
Learning to Generalize One Sample at a Time with Self-Supervision0
Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation0
Learning to Generate the Unknowns for Open-set Domain Adaptation0
Learning to Ignore: Fair and Task Independent Representations0
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects0
Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation0
Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective0
Learning to Learn Recognising Biomedical Entities from Multiple Domains with Task Hardness0
Learning to Match Distributions for Domain Adaptation0
Learning to Optimize Domain Specific Normalization for Domain Generalization0
Learning Topic Representation for SMT with Neural Networks0
Learning to Predict Distributions of Words Across Domains0
Learning to see across Domains and Modalities0
Learning to See in the Dark with Events0
Learning to See Low-Light Images via Feature Domain Adaptation0
Learning to smell for wellness0
Learning to Transfer with von Neumann Conditional Divergence0
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation0
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models0
Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training0
Learning Transferable Features for Speech Emotion Recognition0
Learning Transferrable Representations for Unsupervised Domain Adaptation0
Learning unbiased features0
Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation0
Learning Video Representations of Human Motion From Synthetic Data0
Learning Vision-based Flight in Drone Swarms by Imitation0
Learning Weighted Representations for Generalization Across Designs0
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction0
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains0
Learning Word Sense Distributions, Detecting Unattested Senses and Identifying Novel Senses Using Topic Models0
Learn to Adapt for Monocular Depth Estimation0
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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