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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://bitbucket.org/aauvap/sewer-ml

공식 코드

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

https://git.tu-berlin.de/rsim/CCML

공식 코드

A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels

https://git.tu-berlin.de/rsim/RCML

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Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification

https://github.com/01bb01/ebaychallenge

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Large-Scale Product Retrieval with Weakly Supervised Representation Learning

https://github.com/0liliulei/hieraseg

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Deep Hierarchical Semantic Segmentation

https://github.com/1KE-JI/HierVerb

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Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification

https://github.com/3mcloud/MDACE

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MDACE: MIMIC Documents Annotated with Code Evidence

https://github.com/601175936/homi

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Multi-label Classification with High-rank and High-order Label Correlations

https://github.com/731935354/dia-sum-fact

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Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization

https://github.com/AI-Growth-Lab/Patent-Classification

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PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT

https://github.com/Adamkubany/Multilabel_Semantic_API_comparison

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Comparison of State-of-the-Art Deep Learning APIs for Image Multi-Label Classification using Semantic Metrics

https://github.com/Akimoto-Cris/OASSMLL

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Object-Aware Self-supervised Multi-Label Learning

https://github.com/AlessandroW/A-Knee-Cannot-Have-Lunge-Disease-Out-Of-Distribution-Detection-With-In-Distribution-Voting

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A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification

https://github.com/Alibaba-MIIL/ASL

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Asymmetric Loss For Multi-Label Classification

https://github.com/Alibaba-MIIL/ImageNet21K

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ImageNet-21K Pretraining for the Masses

https://github.com/Alibaba-MIIL/ZS_SDL

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Semantic Diversity Learning for Zero-Shot Multi-label Classification

https://github.com/AlonZolfi/YolOOD

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YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection

https://github.com/AminFadaee/mlculp

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Multi-Label Classification Using Link Prediction

https://github.com/Aviral-03/ViT-Chest-Xray

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A Comparative Study of CNN, ResNet, and Vision Transformers for Multi-Classification of Chest Diseases

https://github.com/AzarKh/ExEm

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ExEm: Expert Embedding using dominating set theory with deep learning approaches

https://github.com/BengaliAI/graphemePrepare

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A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes

https://github.com/CquptZA/Uncertainty_Batch

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Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations

https://github.com/DanieleAlessandro/KENN2

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Neural Networks Enhancement with Logical Knowledge

https://github.com/EGiunchiglia/C-HMCNN

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Coherent Hierarchical Multi-Label Classification Networks

https://github.com/EGiunchiglia/CCN

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Multi-Label Classification Neural Networks with Hard Logical Constraints

https://github.com/Extreme-classification/DECAF

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DECAF: Deep Extreme Classification with Label Features

https://github.com/Extreme-classification/ECLARE

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ECLARE: Extreme Classification with Label Graph Correlations

https://github.com/FBLADL/ACPL

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ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification

https://github.com/GMahlerTheTragic/civic

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Using LLMs to label medical papers according to the CIViC evidence model

https://github.com/GMouYes/DHC_HGL

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Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition

https://github.com/GuoqiangWoodrowWu/MLC-theory

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Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?

https://github.com/GuoqiangWoodrowWu/RBRL

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Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification

https://github.com/HCPLab-SYSU/SSGRL

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Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition

https://github.com/HeoTaksung/Document-to-Sequence-BERT

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Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention

https://github.com/HongzhiQ/SupervisedVsLLM-EfficacyEval

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Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media

https://github.com/IntelVCL/MultiObjectiveOptimization

공식 코드

Multi-Task Learning as Multi-Objective Optimization

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