In this paper, we develop a simple yet effective discriminant multi-label learning (DM2L) method for multi-label learning with missing labels. Specifically, we impose the low-rank structures on all the predicti
Large-scale Multi-label Learning with Missing Labels Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit Dhillon Proceedings of the 31st International Conference on Machine Learning (ICML)|June 2014 Published by Journal of Machine Learning Research ...
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels 1. 多标签学习(Multi-Label Learning)的基本概念 多标签学习是指一个实例可以同时被赋予多个标签的学习任务。例如,在图像分类中,一张图片可能同时包含“猫”和“狗”两个标签。传统的单标签学习只处理一个实例对应一个标签的情况,而多...
Multi-label: there exist many labels, and one instance may have more than one labels. Weak-label: multi-Label Learning With Missing Labels. Partial Multi-label: In practice, the complicated structure of the label space usually makes it hard to decide some hard labels are relevant or not. Pa...
Improving multi-label learning with missing labels by structured semantic correlations. Yang H,Zhou JT,Cai J. Proc.of the14th European Conf.on Computer Vision . 2016H. Yang, J. T. Zhou, and J. Cai. Improving Multi-label Learning with Missing Labels by Structured Semantic Cor- relations. ...
Multi-label learningIncomplete and noisy labelsCost-sensitiveLow-rank and sparseLabel correlationsWeakly-supervised multi-label learning has received much attention more recently, and most of the existing methods focus on such problem with either missing or noisy labels, while the issue with both ...
Paper2:《Simple and Robust Loss Design for Multi-Label Learning with Missing Labels》 ArXiv 2021. 第一篇是做每张图只有一个类别打上了pos label,其他类别都没给label。 本文从梯度的角度,提出了熵最大化loss,目的是为了让没有标注的label预测可以更不确定一些。
In multi-label learning, since different labels may have some distinct characteristics of their own, multi-label learning approach with label-specific features named LIFT has been proposed. However, the construction of label-specific features may encounter the increasing of feature dimensionalities and ...
[IEEE 2015 IEEE International Conference on Computer Vision (ICCV) - Santiago, Chile (2015.12.7-2015.12.13)] 2015 IEEE International Conference on Computer Vision (ICCV) - ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph ...
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels 3 Sep 2022 · Zhongchen Ma, Lisha Li, Qirong Mao, Songcan Chen · Edit social preview Contrastive learning (CL) has shown impressive advances in image representation learning in whichever supervised multi-...