Label Enhancement via Joint Implicit Representation Clustering

Yunan Lu, Weiwei Li, Xiuyi Jia
{"title":"Label Enhancement via Joint Implicit Representation Clustering","authors":"Yunan Lu, Weiwei Li, Xiuyi Jia","doi":"10.24963/ijcai.2023/447","DOIUrl":null,"url":null,"abstract":"Label distribution is an effective label form to portray label polysemy (i.e., the cases that an instance can be described by multiple labels simultaneously). However, the expensive annotating cost of label distributions limits its application to a wider range of practical tasks. Therefore, LE (label enhancement) techniques are extensively studied to solve this problem. Existing LE algorithms mostly estimate label distributions by the instance relation or the label relation. However, they suffer from biased instance relations, limited model capabilities, or suboptimal local label correlations. Therefore, in this paper, we propose a deep generative model called JRC to simultaneously learn and cluster the joint implicit representations of both features and labels, which can be used to improve any existing LE algorithm involving the instance relation or local label correlations. Besides, we develop a novel label distribution recovery module, and then integrate it with JRC model, thus constituting a novel generative label enhancement model that utilizes the learned joint implicit representations and instance clusters in a principled way. Finally, extensive experiments validate our proposal.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Joint Conference on Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24963/ijcai.2023/447","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Label distribution is an effective label form to portray label polysemy (i.e., the cases that an instance can be described by multiple labels simultaneously). However, the expensive annotating cost of label distributions limits its application to a wider range of practical tasks. Therefore, LE (label enhancement) techniques are extensively studied to solve this problem. Existing LE algorithms mostly estimate label distributions by the instance relation or the label relation. However, they suffer from biased instance relations, limited model capabilities, or suboptimal local label correlations. Therefore, in this paper, we propose a deep generative model called JRC to simultaneously learn and cluster the joint implicit representations of both features and labels, which can be used to improve any existing LE algorithm involving the instance relation or local label correlations. Besides, we develop a novel label distribution recovery module, and then integrate it with JRC model, thus constituting a novel generative label enhancement model that utilizes the learned joint implicit representations and instance clusters in a principled way. Finally, extensive experiments validate our proposal.
基于联合隐式表示聚类的标签增强
标签分布是描述标签多义(即一个实例可以被多个标签同时描述的情况)的一种有效的标签形式。然而,标签分布昂贵的注释成本限制了它在更广泛的实际任务中的应用。因此,LE(标签增强)技术被广泛研究来解决这个问题。现有的LE算法大多通过实例关系或标签关系来估计标签分布。然而,它们受到有偏差的实例关系、有限的模型功能或次优的局部标签相关性的影响。因此,在本文中,我们提出了一种称为JRC的深度生成模型来同时学习和聚类特征和标签的联合隐式表示,该模型可用于改进现有的任何涉及实例关系或局部标签相关性的LE算法。此外,我们开发了一种新的标签分布恢复模块,并将其与JRC模型集成,从而构成了一种新的生成式标签增强模型,该模型有原则地利用了学习到的联合隐式表示和实例聚类。最后,大量的实验验证了我们的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信