Multi-Label Classification
374 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
ImageNet-21K Pretraining for the Masses
ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks.
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
amzn/pecos
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ICLR 2022
We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss
billy-inn/NFETC
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NAACL 2018
The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text.
Learning Approximate Inference Networks for Structured Prediction
Prior work used gradient descent for inference, relaxing the structured output to a set of continuous variables and then optimizing the energy with respect to them.
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
Ludwig: a type-based declarative deep learning toolbox
uber/ludwig
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In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code.
TResNet: High Performance GPU-Dedicated Architecture
rwightman/pytorch-image-models
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In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
Distribution-Free, Risk-Controlling Prediction Sets
While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making.
Deep Hierarchical Semantic Segmentation
0liliulei/hieraseg
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CVPR 2022
In this paper, we instead address hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise description of visual observation in terms of a class hierarchy.
PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection
lutao2021/pidray
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Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion.
