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 36013650 of 6439 papers

TitleStatusHype
Semantic Image Segmentation: Two Decades of Research0
On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks0
Semantic Representations for Domain Adaptation: A Case Study on the Tree Kernel-based Method for Relation Extraction0
Semantics-Aware Image to Image Translation and Domain Transfer0
Semantics, Distortion, and Style Matter: Towards Source-free UDA for Panoramic Segmentation0
Semantics Distortion and Style Matter: Towards Source-free UDA for Panoramic Segmentation0
Semantic Segmentation for Real-World and Synthetic Vehicle's Forward-Facing Camera Images0
Semantic Segmentation in Multiple Adverse Weather Conditions with Domain Knowledge Retention0
Semantic Segmentation of highly class imbalanced fully labelled 3D volumetric biomedical images and unsupervised Domain Adaptation of the pre-trained Segmentation Network to segment another fully unlabelled Biomedical 3D Image stack0
Semantic Style Transfer for Enhancing Animal Facial Landmark Detection0
SemEval-2017 Task 12: Clinical TempEval0
SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data0
Seeking Flat Minima with Mean Teacher on Semi- and Weakly-Supervised Domain Generalization for Object Detection0
SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation0
Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps0
Semi-Self-Supervised Domain Adaptation: Developing Deep Learning Models with Limited Annotated Data for Wheat Head Segmentation0
Semi-Supervised Chinese Word Segmentation Using Partial-Label Learning With Conditional Random Fields0
Semi-supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In-domain Data0
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations0
Semi-supervised Domain Adaptation for Semantic Segmentation0
Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network0
Semi-Supervised Domain Adaptation for Semantic Segmentation of Roads from Satellite Images0
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation0
Semi-supervised Domain Adaptation in Graph Transfer Learning0
Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training0
Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment0
Semi-supervised Domain Adaptation with Instance Constraints0
Semi-Supervised Domain Adaptation With Subspace Learning for Visual Recognition0
Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss0
Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning0
IIDM: Inter and Intra-domain Mixing for Semi-supervised Domain Adaptation in Semantic Segmentation0
Semi-Supervised Domain Adaptation with Non-Parametric Copulas0
Semi-supervised Drifted Stream Learning with Short Lookback0
Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition0
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation0
Semi-supervised Large-scale Fiber Detection in Material Images with Synthetic Data0
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation0
Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings0
Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners0
Semi-Supervised QA with Generative Domain-Adaptive Nets0
Semi-supervised Representation Learning for Domain Adaptation using Dynamic Dependency Networks0
Semi-Supervised Representation Learning for Cross-Lingual Text Classification0
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference0
Semi-Supervised Transfer Boosting (SS-TrBoosting)0
SemST: Semantically Consistent Multi-Scale Image Translation via Structure-Texture Alignment0
SenseSpotting: Never let your parallel data tie you to an old domain0
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation0
Sentence Embedding for Neural Machine Translation Domain Adaptation0
Sentence Rephrasing for Parsing Sentences with OOV Words0
Sentence Weighting for Neural Machine Translation Domain Adaptation0
Show:102550
← PrevPage 73 of 129Next →

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