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CapsuleGAN: Generative Adversarial Capsule Network

  • Ayush JaiswalEmail author
  • Wael AbdAlmageed
  • Yue Wu
  • Premkumar Natarajan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)

Abstract

We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adversarial network (GAN) setting, while modeling image data. We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models. We show that CapsuleGAN outperforms convolutional-GAN at modeling image data distribution on MNIST and CIFAR-10 datasets, evaluated on the generative adversarial metric and at semi-supervised image classification.

Keywords

Capsule networks Generative adversarial networks 

Notes

Acknowledgements

This work is based on research sponsored by the Defense Advanced Research Projects Agency under agreement number FA8750-16-2-0204. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense Advanced Research Projects Agency or the U.S. Government.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ayush Jaiswal
    • 1
    Email author
  • Wael AbdAlmageed
    • 1
  • Yue Wu
    • 1
  • Premkumar Natarajan
    • 1
  1. 1.USC Information Sciences InstituteMarina del ReyUSA

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