SOTAVerified

Multi-Label Classification

multilabel graph classification with highest result

Papers

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Showing 926–950 of 1198 papers

TitleStatusHype
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification—0
ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter—0
Reliable Multi-label Classification: Prediction with Partial Abstention—0
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label ClassificationCode0
Neural Message Passing for Multi-Label ClassificationCode1
Ranking-Based Autoencoder for Extreme Multi-label Classification—0
Elucidating image-to-set prediction: An analysis of models, losses and datasetsCode0
Adapting RNN Sequence Prediction Model to Multi-label Set Prediction—0
G-softmax: Improving Intra-class Compactness and Inter-class Separability of FeaturesCode0
Multi-View Matrix Completion for Multi-Label Image Classification—0
Multi-Label Image Recognition with Graph Convolutional NetworksCode0
Comparison of State-of-the-Art Deep Learning APIs for Image Multi-Label Classification using Semantic MetricsCode0
Imbalanced multi-label classification using multi-task learning with extractive summarizationCode0
A Sequential Set Generation Method for Predicting Set-Valued Outputs—0
Learning a Deep ConvNet for Multi-label Classification with Partial Labels—0
Improving performance and inference on audio classification tasks using capsule networks—0
Robustness Certificates Against Adversarial Examples for ReLU Networks—0
Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classificationCode0
Stochastic Gradient TreesCode0
Plugin Networks for Inference under Partial EvidenceCode0
Multi-Label Adversarial Perturbations—0
A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature GenomeCode0
Group Preserving Label Embedding for Multi-Label Classification—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label RulesCode0
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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-V0 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
6Conditional-Training-LSR-V1AVERAGE AUC ON 14 LABEL0.93—Unverified
7Hierarchical-Learning-V4 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
8YWW(ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
9Multi-Stage-Learning-CNN-V3 (ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified
10DeepCNNsGM(ensemble)AVERAGE AUC ON 14 LABEL0.93—Unverified