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Multi-Label Classification

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Multi-Label Classification is the supervised learning problem where an instance may be associated with multiple labels. This is an extension of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.

Source: Deep Learning for Multi-label Classification

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/JAugusto97/ToLD-Br

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Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis

https://github.com/JoakimEdin/explainable-medical-coding

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An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records

https://github.com/JohnMBrandt/text-classification

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Imbalanced multi-label classification using multi-task learning with extractive summarization

https://github.com/JunwenBai/MPVAE

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Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model

https://github.com/KanghoonYoon/torch-rasgg

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RA-SGG: Retrieval-Augmented Scene Graph Generation Framework via Multi-Prototype Learning

https://github.com/KareemYousrii/SPL

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Semantic Probabilistic Layers for Neuro-Symbolic Learning

https://github.com/Kaylee0501/X-MLClass

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Open-world Multi-label Text Classification with Extremely Weak Supervision

https://github.com/Kikyo-16/Sound_event_detection

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Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection

https://github.com/LADI-Dataset/ladi-overview

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LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery

https://github.com/LalehSeyyed/CheXclusion

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CheXclusion: Fairness gaps in deep chest X-ray classifiers

https://github.com/LiDCC/GuzhengTech99

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Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism

https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21

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Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

https://github.com/LucasFidon/label-set-loss-functions

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Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

https://github.com/Luojiaqimath/ClassbalancedLoss4GBDT

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Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions

https://github.com/Megvii-Nanjing/ML-GCN

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Showing Many Labels in Multi-label Classification Models: An Empirical Study of Adversarial Examples

https://github.com/MemoriesJ/KAMG

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Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs

https://github.com/MengtingXu1203/EvaluatingRobustness

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Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack

https://github.com/MiuLab/ICD-Correlation

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Modeling Diagnostic Label Correlation for Automatic ICD Coding

https://github.com/MrGranddy/GaussianMLR

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RLSEP: Learning Label Ranks for Multi-label Classification

https://github.com/NeuromorphicComputationResearchProgram/Learning-with-Holographic-Reduced-Representations

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Learning with Holographic Reduced Representations

https://github.com/NickyFot/HitchhikersGuide

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A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning

https://github.com/Nishiken1/Circular-HRR

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Multi-label Learning with Random Circular Vectors

https://github.com/OctoberChang/X-Transformer

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Taming Pretrained Transformers for Extreme Multi-label Text Classification

https://github.com/Optimization-AI/LibAUC

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Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

https://github.com/Priberam/mesinesp-svm

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Priberam at MESINESP Multi-label Classification of Medical Texts Task

https://github.com/QData/LaMP

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Neural Message Passing for Multi-Label Classification

https://github.com/RingBDStack/dynamic_network_embedding

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Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

https://github.com/SNL-UCSB/netFound

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netFound: Foundation Model for Network Security

https://github.com/SatyadevNtv/ROT4C

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Efficient Robust Optimal Transport with Application to Multi-Label Classification

https://github.com/Shai128/ui

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Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting

https://github.com/SkBlaz/reliefe

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ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings

https://github.com/SlongLiu/query2labels

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Query2Label: A Simple Transformer Way to Multi-Label Classification

https://github.com/StonyBrookNLP/emotion-label-semantics

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Modeling Label Semantics for Predicting Emotional Reactions

https://github.com/Uchman21/MLGW

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Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification

https://github.com/VRCMF/MARN

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Multitask Balanced and Recalibrated Network for Medical Code Prediction

https://github.com/VRCMF/MT-RAM

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Multitask Recalibrated Aggregation Network for Medical Code Prediction

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.