Multi-Label Classification

164 papers with code • 7 benchmarks • 20 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

Greatest papers with code

TResNet: High Performance GPU-Dedicated Architecture

rwightman/pytorch-image-models 30 Mar 2020

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.

Ranked #4 on Fine-Grained Image Classification on Oxford 102 Flowers (using extra training data)

Fine-Grained Image Classification General Classification +2

Ludwig: a type-based declarative deep learning toolbox

uber/ludwig 17 Sep 2019

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.

Image Captioning Image Classification +12

node2vec: Scalable Feature Learning for Networks

shenweichen/GraphEmbedding 3 Jul 2016

Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

Link Prediction Multi-Label Classification +2

SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis

PaddlePaddle/PaddleNLP 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-Label Classification Sentiment Analysis

Fast Network Embedding Enhancement via High Order Proximity Approximation

benedekrozemberczki/karateclub ‏‏‎ ‎ 2020

Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently.

Dimensionality Reduction Link Prediction +2

ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices

Microsoft/EdgeML ICML 2017

Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.

Multi-Label Classification

A scikit-based Python environment for performing multi-label classification

scikit-multilearn/scikit-multilearn 5 Feb 2017

It provides native Python implementations of popular multi-label classification methods alongside a novel framework for label space partitioning and division.

General Classification Multi-Label Classification

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.

Depth Estimation General Classification +5

Learning to diagnose from scratch by exploiting dependencies among labels

arnoweng/CheXNet 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.

Multi-Label Classification