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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/sepehrbakhshi/ml-bels

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Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification

https://github.com/shannonfenn/Multi-Label-Curricula-via-Minimum-Feature-Selection

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Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks

https://github.com/shantanuj/tdam_top_down_attention_module

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TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs

https://github.com/simonucl/HJCL

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Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification

https://github.com/sm354/mmgn

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IITD at the WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection

https://github.com/snucml/largelossmatters

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Large Loss Matters in Weakly Supervised Multi-Label Classification

https://github.com/sohampoddar26/caves-data

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CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

https://github.com/somsubhra04/aisome_2023

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Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social Media

https://github.com/sonlam1102/job-prediction-multilabel-vietnamese

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Predicting Job Titles from Job Descriptions with Multi-label Text Classification

https://github.com/soumickmj/TorchEsegeta

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Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

https://github.com/soumickmj/diagnoPP

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Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

https://github.com/soumyac1999/hyperbolic-label-emb-for-hmc

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Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification

https://github.com/sprenkamp/llm_propaganda_detection

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Large Language Models for Propaganda Detection

https://github.com/squeezeailab/tool2vec

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Efficient and Scalable Estimation of Tool Representations in Vector Space

https://github.com/ss-sun/Attri-Net-V2

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Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals

https://github.com/ss-sun/attri-net

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Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals

https://github.com/sstonemason/ret-clip

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RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports

https://github.com/stanford-futuredata/lotus

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

https://github.com/stanfordnlp/contract-nli-bert

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ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts

https://github.com/starmemda/MlTr

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MlTr: Multi-label Classification with Transformer

https://github.com/submissionemnlp/llm_propaganda_detection

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Large Language Models for Propaganda Detection

https://github.com/sunanhe/mkt

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Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer

https://github.com/supertyd/XTrack

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XTrack: Multimodal Training Boosts RGB-X Video Object Trackers

https://github.com/superyue72/OptLLM

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OptLLM: Optimal Assignment of Queries to Large Language Models

https://github.com/swarnaHub/multiPRover

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multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning

https://github.com/syednabilashraf/SynthEnsemble

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SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification

https://github.com/szbonaldo/fedmlp

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FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity

https://github.com/takelamag/pu-mlc

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Positive Label Is All You Need for Multi-Label Classification

https://github.com/talbaumel/MIMIC

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Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment

https://github.com/tamim-el/sisokr

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Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels

https://github.com/tayebiarasteh/chestx

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Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels

https://github.com/techmn/satmae_pp

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Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery

https://github.com/th-nuernberg/crisis-tapt-hmc

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Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning

https://github.com/the-mercury/CIHMLC

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Clinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss Function

https://github.com/thomasnguyen92/MIMIC-IV-ICD-data-processing

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Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification

https://github.com/thu-ml/mmdcgm-ssl

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Max-Margin Deep Generative Models for (Semi-)Supervised Learning

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