Multi-Label Classification

322 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.

Source: Deep Learning for Multi-label Classification


Use these libraries to find Multi-Label Classification models and implementations
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Most implemented papers

Densely Connected Convolutional Networks

liuzhuang13/DenseNet 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

aditya-grover/node2vec 3 Jul 2016

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

yaoli/chest_xray_14 ICLR 2018

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 5 Feb 2016

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 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 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 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

dheeraj7596/SCDV EMNLP 2017

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.

ML-Net: multi-label classification of biomedical texts with deep neural networks

jingcheng-du/ML_Net-1 13 Nov 2018

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.

ImageNet-21K Pretraining for the Masses

Alibaba-MIIL/ImageNet21K 22 Apr 2021

ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks.