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
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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
Most implemented papers
Cost-Sensitive Label Embedding for Multi-Label Classification
Furthermore, extensive experimental results demonstrate that CLEMS is significantly better than a wide spectrum of existing LE algorithms and state-of-the-art cost-sensitive algorithms across different cost functions.
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep de-noising auto-encoder (sDAE or aDAE) to generate new data-driven features from the Mel-Filter Banks (MFBs) features.
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.
Deep Label Distribution Learning with Label Ambiguity
However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation.
Learning Features of Music from Scratch
benadar293/benadar293.github.io
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This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research.
A scikit-based Python environment for performing multi-label classification
It provides native Python implementations of popular multi-label classification methods alongside a novel framework for label space partitioning and division.
Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification
Analysis of the learned SRN model demonstrates that it can effectively capture both semantic and spatial relations of labels for improving classification performance.
Food Ingredients Recognition through Multi-label Learning
Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.
A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting
kyeongsoo/indoor_localization
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Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built.
Multi-Label Image Recognition with Graph Convolutional Networks
megvii-research/ml-gcn
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CVPR 2019
The task of multi-label image recognition is to predict a set of object labels that present in an image.
