Conditional Generative Adversarial Capsule Networks

被引：0

作者：

Kong R. ^{[1
,2
]}

Huang G. ^{[2
]}

机构：

[1] School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus), Zhuhai

[2] College of Information Science and Technology, Jinan University, Guangzhou

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 01期

关键词：

Capsule networks (CapsNets); Conditional model; Generative adversarial networks (GAN); Image generation;

D O I：

10.16383/j.aas.c180590

中图分类号：

学科分类号：

摘要：

Generative adversarial networks (GAN) is one of the main methods of learning deep generative models in an unsupervised fashion. The generative modeling method based on a network of differential generators is the hottest research field, but due to the complexity of the real sample distribution, the GAN has many problems in the stability of training process and the quality of generation. In the field of generative modeling, the exploration of network structure is an important research direction. And in this paper, we use the capsule networks (CapsNets) to reconstruct the structure of GAN, and the loss function based on Earth-mover distance proposed in Wasserstein GAN (WGAN) is used in the training process, and then we add the conditional constraints to stabilize the model generation process. According to the above, a conditional generative adversarial capsule networks (C-CapsGAN) is established. The results of multiple simulation experiments on the MINIST and CIFAR-10 datasets show that it is feasible to apply the CapsNets to the generative modeling field. Besides, compared with the existing similar model, C-CapsGAN can not only stably produce high-quality images in the image generation task, but also inhibit the occurrence of pattern collapse more effectively. Copyright © 2020 Acta Automatica Sinica. All rights reserved.

引用

页码：94 / 107

页数：13

共 18 条

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