计算机与现代化Issue(3)：54-60,7.DOI:10.3969/j.issn.1006-2475.2024.03.009
适用于网络新闻数据的未配对跨模态哈希方法
Unpaired Cross-modal Hashing Method for Web News Data
武昭盟 1张成刚2
作者信息
- 1. 长春大学网络空间安全学院,吉林 长春 130012
- 2. 内蒙古民族大学计算机科学与技术学院,内蒙古 通辽 028000
- 折叠
摘要
Abstract
Most of the current cross-modal Hashing methods can only be trained when fully paired instances are provided,and are not suitable for a large number of unpaired data in the real world.In order to solve this problem,an unpaired cross-modal Hashing method for Web news data is proposted.Firstly,a feature fusion network is constructed to process the unpaired training data,the modal information is supplemented and improved,and the adversarial loss is used to strengthen the common representa-tion of learning.Secondly,the affinity matrix optimizes the feature distribution of samples and the generated binary codes,so that the semantic relationship between samples is more explicit.Finally,we add a class prediction loss to enhance the discrimina-tion ability of binary codes.Experiments on real network news datasets with paired scenes and unpaired scenes respectively,the results show that the proposed method can be extended to practical applications.关键词
跨模态哈希/特征融合/未配对数据/对抗性学习Key words
cross-modal Hashing/feature fusion/unpaired data/adversarial learning分类
信息技术与安全科学引用本文复制引用
武昭盟,张成刚..适用于网络新闻数据的未配对跨模态哈希方法[J].计算机与现代化,2024,(3):54-60,7.
