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
Densely Connected Convolutional Networks
liuzhuang13/DenseNet
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CVPR 2017
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
node2vec: Scalable Feature Learning for Networks
Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
Learning to diagnose from scratch by exploiting dependencies among labels
The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures.
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
deep-spin/entmax
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We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities.
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
baidu/Senta
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ACL 2020
In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.
Multi-Task Learning as Multi-Objective Optimization
IntelVCL/MultiObjectiveOptimization
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NeurIPS 2018
These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks.
Asymmetric Loss For Multi-Label Classification
Alibaba-MIIL/ASL
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ICCV 2021
In this paper, we introduce a novel asymmetric loss ("ASL"), which operates differently on positive and negative samples.
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.
Extremely Randomized CNets for Multi-label Classification
Promising approaches for MLC are those able to capture label dependencies by learning a single probabilistic model—differently from other competitive approaches requiring to learn many models.
ML-Net: multi-label classification of biomedical texts with deep neural networks
Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems.
