SOTAVerified

Multi-Label Classification

multilabel graph classification with highest result

Papers

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Showing 751–775 of 1198 papers

TitleStatusHype
t-PINE: Tensor-based Predictable and Interpretable Node Embeddings—0
Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification—0
Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification—0
Transformer models as an efficient replacement for statistical test suites to evaluate the quality of random numbers—0
Trigger-free Event Detection via Derangement Reading Comprehension—0
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification—0
UMUTeam at SemEval-2022 Task 6: Evaluating Transformers for detecting Sarcasm in English and Arabic—0
Unbiased Loss Functions for Extreme Classification With Missing Labels—0
Unbiased Loss Functions for Multilabel Classification with Missing Labels—0
Uncertainty in Extreme Multi-label Classification—0
Understanding Gender and Racial Disparities in Image Recognition Models—0
Understanding Human Context in 3D Scenes by Learning Spatial Affordances with Virtual Skeleton Models—0
Understanding Label Bias in Single Positive Multi-Label Learning—0
Understanding patient complaint characteristics using contextual clinical BERT embeddings—0
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification—0
Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks—0
Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management—0
Unsupervised Paraphrasing Consistency Training for Low Resource Named Entity Recognition—0
Unsupervised Person Re-identification via Multi-label Classification—0
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation—0
User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario—0
Using In-Service Train Vibration for Detecting Railway Maintenance Needs—0
Using Language Models on Low-end Hardware—0
Using Language Models on Low-end Hardware—0
Using Supervised Learning to Classify Metadata of Research Data by Discipline of Research—0
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All datasetsCheXpertMS-COCOPASCAL VOC 2007NUS-WIDEChestX-ray14OpenImages-v6PASCAL VOC 2012MLRSNetMRNetMIMIC-CXR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CFT (ensemble) Macao Polytechnic UniversityAVERAGE AUC ON 14 LABEL0.93—Unverified
2Hierarchical-Learning-V1 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
3DeepAUC-v1AVERAGE AUC ON 14 LABEL0.93—Unverified
4Conditional-Training-LSRAVERAGE AUC ON 14 LABEL0.93—Unverified
5Hierarchical-Learning-V4 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
6YWW(ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
7Hierarchical-Learning-V0 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
8Conditional-Training-LSR-V1AVERAGE AUC ON 14 LABEL0.93—Unverified
9DeepCNNsGM(ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
10Multi-Stage-Learning-CNN-V3 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified