multi-label-learning

ShimShim46/HFT-CNN

Convolutional Neural Network based on Hierarchical Category Structure for Multi-label Short Text Categorization

hellowangqian/multi-label-image-classification

A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN

akshitac8/BiAM

[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

rhgao/Deep-MIML-Network

Learning to Separate Object Sounds by Watching Unlabeled Video (ECCV 2018)

Correr-Zhou/SPML-AckTheUnknown

[ECCV 2022] Offical implementation of the paper "Acknowledging the Unknown for Multi-label Learning with Single Positive Labels".

monk1337/Graph-Neural-networks-for-NLP

Graph Neural networks for NLP

wanglichenxj/Adaptive-Graph-Guided-Embedding-for-Multi-label-Annotation

AG2E: A novel adaptive graph based multi-label learning framework for multi-label annotation, image retrieval, and other applications.

kiyoon/verb_ambiguity

Official implementation of "An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition", BMVC 2022

nirbhayjm/GenEML

Scalable Generative Models for Mullti-label Learning with Missing Labels

kochlisGit/Advanced-ML

Advanced Machine Learning Algorithms including Cost-Sensitive Learning, Class Imbalances, Multi-Label Data, Multi-Instance Learning, Active Learning, Multi-Relational Data Mining, Interpretability in Python using Scikit-Learn.

PranjalSahu/DRML

Deep Region and Multi-label Learning for Facial Action Unit Detection

rodolfomp123/imb-mulan

The Mulan Framework with Multi-Label Resampling Algorithms

YuanchenBei/Awesome-Multi-Label-Learning-On-Graphs

A curated list of papers on multi-label learning on graphs (MLLG).

amjadseyedi/SPMLD

Self-Paced Multi-Label Learning with Diversity

hallamlab/mlLGPR

Metabolic pathway inference using multi-label classification with rich pathway features

ChristianSch/skml

scikit-learn compatibel multi-label classification

Lxinyuelxy/multi-label-learn

saisrivatsan/tf-protoNN

Tensorflow ProtoNN for Multi-label learning (supports both single/multi-gpu usage)

zhaodwahu/LSGL

code of LSGL

JiaWu-Repository/MSFS

[IEEE Transactions on Multimedia 2020] Multi-View Multi-Label Learning With Sparse Feature Selection for Image Annotation

arbasher/straSplit

Stratification of multi-label datasets

hallamlab/reMap

reMap: relabeling metabolic pathway data with groups to improve prediction outcomes

hallamlab/triUMPF

Metabolic pathway inference using non-negative matrix factorization with community detection

KouYinan/ALC-MFS

feature selection

ml-lab-sau/Multi-label-learning-with-missing-labels-using-sparse-global-structure-for-label-specific-features

To deal with the issues emerging from incomplete labels and high-dimensional input space, we propose a multi-label learning approach based on identifying the label-specific features and constraining them with a sparse global structure. The sparse structural constraint helps maintain the typical characteristics of the multi-label learning data.

h-ehsan/EMLNN

Ensemble-based Multi-Label Neural Network (EMLNN)

hallamlab/leADS

leADS: improved metabolic pathway inference based on active dataset subsampling

illusionic/Multi_label-Image-Classification-Using-Automated-Approach

Multi-label Image Classification using Automated Approach.

JoshWarn/Multi-Label-Shapes-Toy-Dataset-Generator

An easy-to-use multi-label image dataset generator.

ml-lab-sau/Auxiliary-Label-Embedding-for-Multi-label-Learning-with-Missing-Labels

In this paper, we propose an approach for multi-label classification when label details are incomplete by learning auxiliary label matrix from the observed labels, and generating an embedding from learnt label correlations preserving the correlation structure in model coefficients.

ml-lab-sau/BT-MA

BT-MA is a Multi-label Machine learning model

ml-lab-sau/Discriminatory-Label-specific-Weights-for-Multi-label-Learning-with-Missing-Labels

To deal with the class imbalance problem in multi-label learning with missing labels, we propose Class Imbalance aware Missing labels Multi-label Learning, CIMML. Our proposed method handles class imbalance issue by constructing a label weight matrix with weight estimation guided by how frequently a label is present, absent, and unobserved.

muberraozmen/BNCL

Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification (CoLLAs 2023)

Sagnik07/CraftML-An-Efficient-Clustering-based-Random-Forest-for-Extreme-Multi-label-Learning.

We explore extreme multi label learning using a random forest based algorithm. The parallelized implementation uses a K-Means clustering based partitioning approach to improve performance.