摘要
同一事物通常需要从不同角度进行表达.然而,现实应用经常引出复杂的场景,导致完整视图数据很难获得.因此研究如何构建事物的完整视图具有重要意义.本文提出一种基于生成对抗网络(Generative adversarial networks,GAN)的多视图学习与重构算法,利用已知单一视图,通过生成式方法构建其他视图.为构建多视图通用的表征,提出新型表征学习算法,使得同一实例的任意视图都能映射至相同的表征向量,并保证其包含实例的重构信息.为构建给定事物的多种视图,提出基于生成对抗网络的重构算法,在生成模型中加入表征信息,保证了生成视图数据与源视图相匹配.所提出的算法的优势在于避免了不同视图间的直接映射,解决了训练数据视图不完整问题,以及构造视图与已知视图正确对应问题.在手写体数字数据集MNIST,街景数字数据集SVHN和人脸数据集CelebA上的模拟实验结果表明,所提出的算法具有很好的重构性能.
Generally, objects often require to represent in different views. However, real-world applications in complex scenarios can hardly have complete views of a given object. In this paper, we propose generative adversarial network（GAN） based multi-view learning and reconstruction algorithms. A novel representation learning algorithm is proposed,which guarantees different views of the same object are mapped to the same representation. Meanwhile, the algorithm guarantees the representation carries enough reconstructed information. To construct multi-views of a given object, a generative adversarial network based reconstruction algorithm is proposed, which includes the representation information in the generation and discrimination models to guarantee the constructed views perfectly map the source view. The merits of the proposed algorithms lie in the fact that they avoid direct mapping among different views, and can solve the problem of missing views in training data and the problem of mapping between constructed views and the source views. Simulated experiments on handwritten digit dataset（MNIST）, street view house numbers dataset（SVHN） and Celeb Faces attributes dataset（CelebA） indicate that the proposed algorithms yield satisfactory reconstruction performances.
作者
孙亮
韩毓璇
康文婧
葛宏伟
SUN Liang;HAN Yu-Xuan;KANG Wen-Jing;GE Hong-Wei(College of Computer Science and Technology, Dalian University of Technology, Dalian 116023)
出处
《自动化学报》
EI
CSCD
北大核心
2018年第5期819-828,共10页
Acta Automatica Sinica
基金
国家自然科学基金(61402076
61572104
61103146)
吉林大学符号计算与知识工程教育部重点实验室项目(93K172017K03)
中央高校基本科研业务项目(DUT17JC04)资助~~
关键词
多视图重构
条件生成对抗网络
多视图表征学习
生成模型
Multi-view reconstruction
conditional generative adversarial networks （CGAN）
multi-view representationlearning
generative models
