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Multi-Label Classification

76 papers with code · Methodology

Multi-Label Classification is the supervised learning problem where an instance may be associated with multiple labels. This is opposed to the traditional task 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

Benchmarks

Greatest papers with code

Deep Residual Learning for Image Recognition

CVPR 2016 tensorflow/models

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

DOMAIN GENERALIZATION FINE-GRAINED IMAGE CLASSIFICATION IMAGE-TO-IMAGE TRANSLATION MULTI-LABEL CLASSIFICATION OBJECT DETECTION PERSON RE-IDENTIFICATION SEMANTIC SEGMENTATION

TResNet: High Performance GPU-Dedicated Architecture

30 Mar 2020rwightman/pytorch-image-models

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.

FINE-GRAINED IMAGE CLASSIFICATION MULTI-LABEL CLASSIFICATION OBJECT DETECTION

node2vec: Scalable Feature Learning for Networks

3 Jul 2016shenweichen/GraphEmbedding

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 NODE CLASSIFICATION REPRESENTATION LEARNING

SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis

ACL 2020 baidu/Senta

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

‏‏‎ ‎ 2020 benedekrozemberczki/karateclub

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

DIMENSIONALITY REDUCTION LINK PREDICTION MULTI-LABEL CLASSIFICATION NETWORK EMBEDDING

ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices

ICML 2017 Microsoft/EdgeML

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

MULTI-LABEL CLASSIFICATION

Multi-Label Image Recognition with Graph Convolutional Networks

CVPR 2019 Megvii-Nanjing/ML_GCN

The task of multi-label image recognition is to predict a set of object labels that present in an image.

MULTI-LABEL CLASSIFICATION WORD EMBEDDINGS

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

5 Feb 2017scikit-multilearn/scikit-multilearn

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

MULTI-LABEL CLASSIFICATION

Learning to diagnose from scratch by exploiting dependencies among labels

ICLR 2018 arnoweng/CheXNet

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