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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/jiweibo/imagenet

Densely Connected Convolutional Networks

https://github.com/jlgarridol/scikit-multilearn-ubumlaas

A scikit-based Python environment for performing multi-label classification

https://github.com/jm12138/Paddle-CheXNet

Learning to diagnose from scratch by exploiting dependencies among labels

https://github.com/jmesich/chestxray

Densely Connected Convolutional Networks

https://github.com/johntd54/stanford_car

Densely Connected Convolutional Networks

https://github.com/jonnor/datascience-master

Densely Connected Convolutional Networks

https://github.com/jthickstun/thickstun2017learning

Learning Features of Music from Scratch

https://github.com/karandesaiii/CheXNet

Learning to diagnose from scratch by exploiting dependencies among labels

https://github.com/kareldo/biodex

In-Context Learning for Extreme Multi-Label Classification

https://github.com/kclip/r_autoeval_plus

Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control

https://github.com/kingcong/densenet

Densely Connected Convolutional Networks

https://github.com/krishnakarthi/COVID-19_Prediction

Densely Connected Convolutional Networks

https://github.com/kriskorrel/sparsemax-pytorch

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

https://github.com/kshen3778/Ingredient-Detection

Food Ingredients Recognition through Multi-label Learning

https://github.com/kyeongsoo/indoor_localization

A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting

https://github.com/kyriemao/TagGNN-SIGIR

Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach

https://github.com/lab-midas/med_segmentation

Densely Connected Convolutional Networks

https://github.com/lgerrets/asait18-tagging

Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging

https://github.com/lifu-tu/ENGINE

Learning Approximate Inference Networks for Structured Prediction

https://github.com/lifu-tu/INFNET

Learning Approximate Inference Networks for Structured Prediction

https://github.com/lingorx/hieraseg

Deep Hierarchical Semantic Segmentation

https://github.com/lionelmessi6410/ntga

Densely Connected Convolutional Networks

https://github.com/liuzhuang13/DenseNetCaffe

Densely Connected Convolutional Networks

https://github.com/livingbody/paddlenlp_sentiment

SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis

https://github.com/liyu10000/pneumoconiosis

Learning to diagnose from scratch by exploiting dependencies among labels

https://github.com/lpirola13/flower-recognizer

Densely Connected Convolutional Networks

https://github.com/lpirola13/flower_recognizer

Densely Connected Convolutional Networks

https://github.com/ludwig-ai/ludwig

Ludwig: a type-based declarative deep learning toolbox

https://github.com/manuelladron/Ambiguity-in-Computational-Creativity

Multimodal Word Sense Disambiguation in Creative Practice

https://github.com/marload/ConvNets-TensorFlow2

Densely Connected Convolutional Networks

https://github.com/mathkey/mssnet

Multi-Label Classification with Label Graph Superimposing

https://github.com/matinmoezzi/ups_conformal_classification

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

https://github.com/maxium0526/category-wise-fine-tuning

https://github.com/maxium0526/cft-chexpert

https://github.com/megvii-research/ml-gcn

Multi-Label Image Recognition with Graph Convolutional Networks

https://github.com/meowbutlerdev/KSSNet

Multi-Label Classification with Label Graph Superimposing

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