GAN Using Capsule Network for Discriminator and Generator

Kanako Marusaki, Hiroshi Watanabe

研究成果: Conference contribution

抄録

In this paper, we propose Capsule GAN, which incorporates the capsule network into the structure of both discriminator and generator of Generative Adversarial Networks (GAN). Many CNN-based GANs have been studied. Among them, Deep Convolutional GAN (DCGAN) has been attracting particular attention. Other examples include convolutional GAN, auxiliary classifier GAN, Wasserstein GAN (WGAN) which uses Wasserstein distance to prevent mode collapse during the learning process, and Wasserstein GAN-gp (WGAN-gp). However, image generation by GAN is not stable and prone to mode collapse. As a result, the quality of the generated images is not satisfactory. It is expected to generate better quality images by incorporating a capsule network, which compensates for the shortcomings of CNN, into the structure of GAN. Therefore, in this paper, we propose two approaches to generate images with better quality by incorporating the capsule network into GAN. The experimental results show that the proposed method is superior to the conventional method.

本文言語English
ホスト出版物のタイトル2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ647-650
ページ数4
ISBN(電子版)9781665436762
DOI
出版ステータスPublished - 2021
イベント10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
継続期間: 2021 10月 122021 10月 15

出版物シリーズ

名前2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
国/地域Japan
CityKyoto
Period21/10/1221/10/15

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 信号処理
  • 生体医工学
  • 電子工学および電気工学
  • メディア記述
  • 器械工学

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