Interactive Multi-Label CNN Learning with Partial Labels IMLC, CVPR, 2020 考虑一般的 cross-entropy 对 missing label 的情况不是很鲁棒,本文通过学习label-instace的相似度图对 cross-entropy 进行正则,缓解missing label 的影响;并且本文的方法是 interactive 的,也就是说分类器和学习相似度图这两个过程是想...
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。 如下图所示,针对该问题,最简单的bsl是我...
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 predictions of instances from the same labels (local shrinking of rank), and a maximally ...
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 ...
（1）LEML：Large-scale Multi-label Learning with Missing Labels一种针对弱标记数据的多标记学习方法 ...
摘要 In multi-label learning, feature selection is a non-ignorable preprocessing step which can alleviate the negative eff... 出版源 Springer US , 2019 关键词 Feature interaction / Feature selection / Missing labels / Multi-label ...
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 predictions of instances from the same labels (local shrinking of rank), and a maximally ...
,multiple complementary label learning,streaming label learning,multi-label learning with missing labels...
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 ...
Multi-label learning with missing labels using mixed dependency graphs, 2018. 3 [27] Tong Wu, Qingqiu Huang, Ziwei Liu, Yu Wang, and Dahua Lin. Distribution-balanced loss for multi-label classification in long-tailed datasets, 2020. 1 [28] Hao Yang, Joey Tianyi Zhou, and Jianfei Cai. ...