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 60016050 of 6439 papers

TitleStatusHype
Handling new target classes in semantic segmentation with domain adaptationCode0
Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Tweets for Emergency ServicesCode0
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated ExamplesCode0
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving RegularizationCode0
Cycle-consistent Conditional Adversarial Transfer NetworksCode0
Together We Can: Multilingual Automatic Post-Editing for Low-Resource LanguagesCode0
News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News RecommendationCode0
TOHAN: A One-step Approach towards Few-shot Hypothesis AdaptationCode0
BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic SegmentationCode0
Exploring Object Relation in Mean Teacher for Cross-Domain DetectionCode0
Cycle-Balanced Representation Learning For Counterfactual InferenceCode0
CycDA: Unsupervised Cycle Domain Adaptation from Image to VideoCode0
Semi-supervised Domain Adaptive Structure LearningCode0
Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature AlignmentCode0
Unsupervised and self-adaptative techniques for cross-domain person re-identificationCode0
Unsupervised domain adaptation with non-stochastic missing dataCode0
Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-LabellingCode0
Node-wise Domain Adaptation Based on Transferable Attention for Recognizing Road Rage via EEGCode0
Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical PerspectiveCode0
All about Structure: Adapting Structural Information across Domains for Boosting Semantic SegmentationCode0
Noise transfer for unsupervised domain adaptation of retinal OCT imagesCode0
Exploring Adversarially Robust Training for Unsupervised Domain AdaptationCode0
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment ClassificationCode0
Exploiting Semantic Localization in Highly Dynamic Wireless Networks Using Deep Homoscedastic Domain AdaptationCode0
Learning Smooth Representation for Unsupervised Domain AdaptationCode0
Semi-Supervised Learning by Augmented Distribution AlignmentCode0
CyCADA: Cycle-Consistent Adversarial Domain AdaptationCode0
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio EstimationCode0
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene ClassificationCode0
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance DetectionCode0
Aspect-augmented Adversarial Networks for Domain AdaptationCode0
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine TranslationCode0
Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose EstimationCode0
Semi-Supervised Learning with Pseudo-Negative Labels for Image ClassificationCode0
Experiments with Convolutional Neural Networks for Multi-Label Authorship AttributionCode0
A Simple Domain Shifting Networkfor Generating Low Quality ImagesCode0
Example-based Hypernetworks for Out-of-Distribution GeneralizationCode0
Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced EnsembleCode0
TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for DocumentariesCode0
Examining Temporality in Document ClassificationCode0
Curriculum Domain Adaptation for Semantic Segmentation of Urban ScenesCode0
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-LearningCode0
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud RecognitionCode0
EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis EnvironmentsCode0
Semi-supervised representation learning via dual autoencoders for domain adaptationCode0
Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challengeCode0
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple SourcesCode0
Event Detection and Domain Adaptation with Convolutional Neural NetworksCode0
Evaluating Tokenizers Impact on OOVs Representation with Transformers ModelsCode0
Curriculum based Dropout Discriminator for Domain AdaptationCode0
Show:102550
← PrevPage 121 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
4CMKDAccuracy89Unverified
5PMTransAccuracy89Unverified
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
8DDAAccuracy88.9Unverified
9MEDMAccuracy88.9Unverified
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