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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/logictensornetworks/logictensornetworks

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Logic Tensor Networks

https://github.com/lorry30/TransFA

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TransFA: Transformer-based Representation for Face Attribute Evaluation

https://github.com/lotus-data/lotus

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Semantic Operators: A Declarative Model for Rich, AI-based Data Processing

https://github.com/lsy3/clinical-notes-diagnosis-dl-nlp

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An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes

https://github.com/luka-group/ddr

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Dense Retrieval as Indirect Supervision for Large-space Decision Making

https://github.com/lumia-group/apl

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Asymmetric Polynomial Loss For Multi-Label Classification

https://github.com/lutao2021/pidray

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PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection

https://github.com/lyh-hh/afire

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AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images

https://github.com/manuel-rdz/c-tran

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Multi-Label Retinal Disease Classification using Transformers

https://github.com/marcuscarpenter97/malware-data

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Behavioural Reports of Multi-Stage Malware

https://github.com/masuyama-lab/mlca

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Multi-label Classification via Adaptive Resonance Theory-based Clustering

https://github.com/maxitron93/stratified_sampling_for_XML

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Stratified Sampling for Extreme Multi-Label Data

https://github.com/maxium0526/ptrn

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Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification

https://github.com/me10b031/twitter_user_model

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Choice-Aware User Engagement Modeling andOptimization on Social Media

https://github.com/mendelxu/mixtraining

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Bootstrap Your Object Detector via Mixed Training

https://github.com/michaelyya/wximpactbench

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WXImpactBench: A Disruptive Weather Impact Understanding Benchmark for Evaluating Large Language Models

https://github.com/migueleci/clustering_hmc

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Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification

https://github.com/mikomel/mlcl

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Multi-Label Contrastive Learning for Abstract Visual Reasoning

https://github.com/miladvazan/Aspect-based-Sentiment-Analysis-in-Persian-Reviews

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Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews

https://github.com/mkirchhof/rsl

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pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules

https://github.com/mlvlab/SugaFormer

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Super-class guided Transformer for Zero-Shot Attribute Classification

https://github.com/mmalekzadeh/privacy-preserving-bandits

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Privacy-Preserving Bandits

https://github.com/mmendiet/gfm

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Towards Geospatial Foundation Models via Continual Pretraining

https://github.com/mrT23/TResNet

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TResNet: High Performance GPU-Dedicated Architecture

https://github.com/mrapp-ke/Boomer

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Learning Gradient Boosted Multi-label Classification Rules

https://github.com/mrapp-ke/RuleGeneration

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On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

https://github.com/msadat3/scihtc

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Hierarchical Multi-Label Classification of Scientific Documents

https://github.com/muberraozmen/mrmp

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Multi-relation Message Passing for Multi-label Text Classification

https://github.com/mwydmuch/extremeText

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A no-regret generalization of hierarchical softmax to extreme multi-label classification

https://github.com/mwydmuch/napkinXC

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Probabilistic Label Trees for Extreme Multi-label Classification

https://github.com/mwydmuch/xcolumns

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Generalized test utilities for long-tail performance in extreme multi-label classification

https://github.com/nayeemrizve/ups

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In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

https://github.com/nbalepur/mnemonic

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A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick

https://github.com/ncbi/ml-transformer

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LitMC-BERT: transformer-based multi-label classification of biomedical literature with an application on COVID-19 literature curation

https://github.com/nicholasjohnson2020/discretecompressedsensing.jl

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Compressed Sensing: A Discrete Optimization Approach

https://github.com/nicolas1203/ossgcl

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Contrastive Learning for Online Semi-Supervised General Continual Learning

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