Browse > Methodology > Multi-Label Classification

Multi-Label Classification

49 papers with code · Methodology

State-of-the-art leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

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

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

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

SGM: Sequence Generation Model for Multi-label Classification

COLING 2018 lancopku/SGM

Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.

MULTI-LABEL CLASSIFICATION

Multi-Task Learning as Multi-Objective Optimization

NeurIPS 2018 IntelVCL/MultiObjectiveOptimization

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 INSTANCE SEGMENTATION MULTI-LABEL CLASSIFICATION MULTI-TASK LEARNING SCENE UNDERSTANDING SEMANTIC SEGMENTATION

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

5 Feb 2016vene/sparse-structured-attention

We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities.

MULTI-LABEL CLASSIFICATION NATURAL LANGUAGE INFERENCE

Learning to diagnose from scratch by exploiting dependencies among labels

ICLR 2018 thtang/CheXNet-with-localization

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

DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

8 Sep 2016Refefer/fastxml

In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING

A no-regret generalization of hierarchical softmax to extreme multi-label classification

NeurIPS 2018 mwydmuch/extremeText

Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION