SOTAVerified

Multi-Label Learning

Multi-label learning (MLL) is a generalization of the binary and multi-category classification problems and deals with tagging a data instance with several possible class labels simultaneously [1]. Each of the assigned labels conveys a specific semantic relationship with the multi-label data instance [2, 3]. Multi-label learning has continued to receive a lot of research interest due to its practical application in many real-world problems such as recommender systems [4], image annotation [5], and text classification [6].

References:

  1. Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)

  2. Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837

  3. Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38

  4. Bogaert M, Lootens J, Van den Poel D, Ballings M (2019) Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur J Oper Res 279(2):620– 634

  5. Jing L, Shen C, Yang L, Yu J, Ng MK (2017) Multi-label classification by semi-supervised singular value decomposition. IEEE Trans Image Process 26(10):4612–4625

  6. Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979

Papers

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Showing 1–50 of 299 papers

TitleStatusHype
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion RecognitionCode3
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised LearningCode2
Multi-Label Knowledge DistillationCode1
Vision-Language Pseudo-Labels for Single-Positive Multi-Label LearningCode1
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label ClassificationCode1
DECAF: Deep Extreme Classification with Label FeaturesCode1
Pedestrian Attribute Recognition as Label-balanced Multi-label LearningCode1
MIML library: a Modular and Flexible Library for Multi-instance Multi-label LearningCode1
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label LearningCode1
ECLARE: Extreme Classification with Label Graph CorrelationsCode1
Acknowledging the Unknown for Multi-label Learning with Single Positive LabelsCode1
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and AlgorithmCode1
A Survey on Extreme Multi-label LearningCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text DocumentsCode1
MLPD: Multi-Label Pedestrian Detector in Multispectral DomainCode1
Multi-Label Learning from Single Positive LabelsCode1
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label EnhancementCode1
Simple and Robust Loss Design for Multi-Label Learning with Missing LabelsCode1
XFL: Naming Functions in Binaries with Extreme Multi-label LearningCode1
End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label LearningCode1
CheXclusion: Fairness gaps in deep chest X-ray classifiersCode1
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive LearningCode1
Semantic-Aware Dual Contrastive Learning for Multi-label Image ClassificationCode1
Multi-Label Sampling based on Local Label ImbalanceCode1
Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware TransformersCode1
Synthetic Oversampling of Multi-Label Data based on Local Label DistributionCode1
Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment AnalysisCode1
Learning by Minimizing the Sum of Ranked RangeCode0
Simplified and Unified Analysis of Various Learning Problems by Reduction to Multiple-Instance LearningCode0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
Learning a Compressed Sensing Measurement Matrix via Gradient UnrollingCode0
Semi-supervised Vector-valued Learning: Improved Bounds and AlgorithmsCode0
A hybrid algorithm for Bayesian network structure learning with application to multi-label learningCode0
Incremental Sparse Bayesian Ordinal RegressionCode0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical DocumentsCode0
Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained ModelCode0
LIFT : Multi-Label Learning with Label-Specific FeaturesCode0
Extreme Multi-label Learning for Semantic Matching in Product SearchCode0
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label LearningCode0
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image ClassificationCode0
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label SpacesCode0
Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced LabelsCode0
Discovering Multi-Label Actor-Action Association in a Weakly Supervised SettingCode0
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction PredictionCode0
Discriminatory Label-specific Weights for Multi-label Learning with Missing LabelsCode0
A Simple but Effective Closed-form Solution for Extreme Multi-label LearningCode0
Boosting Single Positive Multi-label Classification with Generalized Robust LossCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified