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 38513900 of 6439 papers

TitleStatusHype
Unsupervised Energy-based Adversarial Domain Adaptation for Cross-domain Text Classification0
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains0
Unsupervised Federated Domain Adaptation for Segmentation of MRI Images0
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow0
Unsupervised Latent Space Translation Network0
Unsupervised Learning of Dictionaries of Hierarchical Compositional Models0
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies0
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Mover’s discrepancies0
Unsupervised Linguistically-Driven Reliable Dependency Parses Detection and Self-Training for Adaptation to the Biomedical Domain0
Unsupervised Model Adaptation for Source-free Segmentation of Medical Images0
Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders0
Unsupervised multi-source domain adaptation for person re-identification via feature fusion and pseudo-label refinement0
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach0
Unsupervised Multi-View Post-OCR Error Correction With Language Models0
Unsupervised Natural Image Patch Learning0
Unsupervised Neural Domain Adaptation for Document Image Binarization0
Unsupervised Noise adaptation using Data Simulation0
Unsupervised Online Multitask Learning of Behavioral Sentence Embeddings0
Unsupervised Person Re-identification by Deep Learning Tracklet Association0
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective0
Unsupervised Reinforcement Adaptation for Class-Imbalanced TextClassification0
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training0
Unsupervised Robust Domain Adaptation without Source Data0
Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge0
Unsupervised Sound Separation Using Mixture Invariant Training0
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer0
Plug-and-Play Transformer Modules for Test-Time Adaptation0
Unsupervised Transductive Domain Adaptation0
Unsupervised Transfer Learning with Self-Supervised Remedy0
Unsupervised Vehicle Counting via Multiple Camera Domain Adaptation0
Unsupervised Vehicle Re-Identification Based on Cross-Style Semi-Supervised Pre-Training and Feature Cross-Division0
Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation0
Unveiling Class-Labeling Structure for Universal Domain Adaptation0
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation0
UoW: Multi-task Learning Gaussian Process for Semantic Textual Similarity0
UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes0
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models0
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation0
Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation0
User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation0
User Satisfaction Modeling with Domain Adaptation in Task-oriented Dialogue Systems0
Uses of Monolingual In-Domain Corpora for Cross-Domain Adaptation with Hybrid MT Approaches0
Using a Goodness Measurement for Domain Adaptation: A Case Study on Chinese Word Segmentation0
Using BabelNet to Improve OOV Coverage in SMT0
Using Comparable Corpora to Adapt MT Models to New Domains0
Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition0
Using Global Land Cover Product as Prompt for Cropland Mapping via Visual Foundation Model0
Using Large Language Models for the Interpretation of Building Regulations0
Using Latent Codes for Class Imbalance Problem in Unsupervised Domain Adaptation0
Using Machine Learning for Particle Identification in ALICE0
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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