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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/VICO-UoE/KD4MTL

Multi-Task Learning as Multi-Objective Optimization

https://github.com/Vamshi399/CarND-Vehicle-Detection

Densely Connected Convolutional Networks

https://github.com/VinayBN8997/CIFAR-10-object-detection

Densely Connected Convolutional Networks

https://github.com/WiktorJ/msnode2vec

node2vec: Scalable Feature Learning for Networks

https://github.com/Xflick/EEND_PyTorch

End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification

https://github.com/Xtra-Computing/VertiBench

Split Learning for collaborative deep learning in healthcare

https://github.com/YCAyca/CLIP_ML_Decoder

CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation

https://github.com/Yeeun-Lee/parrot_proj1

Densely Connected Convolutional Networks

https://github.com/Yeeun-Lee/parrot_vision_competition

Densely Connected Convolutional Networks

https://github.com/YerevaNN/DIIN-in-Keras

Densely Connected Convolutional Networks

https://github.com/YixuanLi/densenet-tensorflow

Densely Connected Convolutional Networks

https://github.com/Yochengliu/MLIC-KD-WSD

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

https://github.com/YoungXiyuan/DCA

Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss

https://github.com/YutingLi0606/SURE

ImageNet-21K Pretraining for the Masses

https://github.com/ZFT-CQU/DSDL

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

https://github.com/ZacharyChenpk/Local_ETHZ

Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss

https://github.com/aangelopoulos/ppi_py

Cross-Prediction-Powered Inference

https://github.com/aaronvvv/sentiment_analysis

SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis

https://github.com/aced125/sparsemax

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

https://github.com/adamzenith/mapie

Distribution-Free, Risk-Controlling Prediction Sets

https://github.com/agassi4013/Log-DenseNet-Tensorflow

Densely Connected Convolutional Networks

https://github.com/ahmedbesbes/mrnet

https://github.com/aimbsg/EVA4_S15

Densely Connected Convolutional Networks

https://github.com/albarji/neurowriter

Densely Connected Convolutional Networks

https://github.com/alexgcsa/AutoMLC

Multi-label classification search space in the MEKA software

https://github.com/andreasveit/densenet-pytorch

Densely Connected Convolutional Networks

https://github.com/anskarl/LoMRF

Semi-Supervised Online Structure Learning for Composite Event Recognition

https://github.com/arbasher/straSplit

Stratified Sampling for Extreme Multi-Label Data

https://github.com/arnavdodiedo/DenseNet-Fashion-MNIST

Densely Connected Convolutional Networks

https://github.com/arnavdodiedo/DenseNet-MNIST

Densely Connected Convolutional Networks

https://github.com/arnoweng/CheXNet

Learning to diagnose from scratch by exploiting dependencies among labels

https://github.com/asad-62/IVP-DNN

Densely Connected Convolutional Networks

https://github.com/asprenger/keras_fc_densenet

Densely Connected Convolutional Networks

https://github.com/atapour/ransomware-classification

Densely Connected Convolutional Networks

https://github.com/averyfallson/rmffn

Query2Label: A Simple Transformer Way to Multi-Label Classification

https://github.com/avm-debatr/bagganhq

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

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