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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/theGuyWithBlackTie/Deep-Extreme-Multi-Label-Learning

Deep Extreme Multi-label Learning

https://github.com/thibaudmartinez/node2vec

node2vec: Scalable Feature Learning for Networks

https://github.com/thtang/CheXNet-with-localization

Learning to diagnose from scratch by exploiting dependencies among labels

https://github.com/titu1994/DenseNet

Densely Connected Convolutional Networks

https://github.com/tommasocarraro/ltntorch

Logic Tensor Networks

https://github.com/tomtung/omikuji

Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification

https://github.com/torchjd/torchjd

Multi-Task Learning as Multi-Objective Optimization

https://github.com/tyliupku/Arbitrary-Order-Infnet

Learning Approximate Inference Networks for Structured Prediction

https://github.com/urielsinger/ctdne

node2vec: Scalable Feature Learning for Networks

https://github.com/urielsinger/fairwalk

node2vec: Scalable Feature Learning for Networks

https://github.com/uva-dsa/ems-pipeline

DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction

https://github.com/vene/sparse-structured-attention

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

https://github.com/vohoaiviet/indoor_localization

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

https://github.com/wangbinglin1995/tianchi

Densely Connected Convolutional Networks

https://github.com/weiwang2330/sparse-structured-attention

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

https://github.com/whitepurple/HBM-loss-for-HTC

ZLPR: A Novel Loss for Multi-label Classification

https://github.com/wikke/Tianchi-Medical-LungTumorDetect

Densely Connected Convolutional Networks

https://github.com/xmc-aalto/dismec

DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

https://github.com/xufanxiong/classification-of-caltech-256

Densely Connected Convolutional Networks

https://github.com/xypan1232/iDeepE

Densely Connected Convolutional Networks

https://github.com/yangyucheng000/densenet/blob/main/densenet.py

Densely Connected Convolutional Networks

https://github.com/yichigo/Chest-X-Ray

Densely Connected Convolutional Networks

https://github.com/yoshitomo-matsubara/torchdistill

Densely Connected Convolutional Networks

https://github.com/yu-gi-oh-leilei/IDA_2023ICLR

https://github.com/yusufsugiono/klasifikasi-gambar-bunga

Densely Connected Convolutional Networks

https://github.com/yw0nam/DenseNet

Densely Connected Convolutional Networks

https://github.com/yzhuoning/DeepAUC_OGB_Challenge

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

https://github.com/zjZSTU/ResNet

Densely Connected Convolutional Networks

https://github.com/zongwei97/untrack

XTrack: Multimodal Training Boosts RGB-X Video Object Trackers

https://gitlab.com/birder/birder

Densely Connected Convolutional Networks

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