Multi-Label Classification
375 papers with code • 10 benchmarks • 28 datasets
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.
Benchmarks
These leaderboards are used to track progress in Multi-Label Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Multi-Label Classification models and implementationsDatasets
MS COCO
CheXpert
NUS-WIDE
ChestX-ray14
MIMIC-CXR
PASCAL VOC 2007
ECHR
Friedman1
VizWiz-Classification
Subtasks
Latest papers with no code
Hierarchical Multi-Label Classification of Online Vaccine Concerns
Vaccine concerns are an ever-evolving target, and can shift quickly as seen during the COVID-19 pandemic.
Deep Learning for Multi-Label Learning: A Comprehensive Survey
Multi-label learning is a rapidly growing research area that aims to predict multiple labels from a single input data point.
The Right Model for the Job: An Evaluation of Legal Multi-Label Classification Baselines
Multi-Label Classification (MLC) is a common task in the legal domain, where more than one label may be assigned to a legal document.
Attention-Based Recurrent Neural Network For Automatic Behavior Laying Hen Recognition
To do this, we first collected and annotated laying hen call signals, then designed an optimal acoustic characterization based on the combination of time and frequency domain features.
Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality
This work introduces a framework to address the computational complexity inherent in Mixed-Integer Programming (MIP) models by harnessing the potential of deep learning.
EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation
As virtual environments continue to advance, the demand for immersive and emotionally engaging experiences has grown.
Class-Incremental Learning for Multi-Label Audio Classification
Experiments are performed on a dataset with 50 sound classes, with an initial classification task containing 30 base classes and 4 incremental phases of 5 classes each.
Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification
Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision.
Aspect category learning and sentimental analysis using weakly supervised learning
In this study, we deployed hybrid models, namely BiLSTM, CNN-BiLSTM, and CNN-LSTM, which harness multiple inputs, including review text, aspect terms, and ratings.
Automated Clinical Coding for Outpatient Departments
Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records.
