# Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks

arXiv: https://arxiv.org/abs/1806.03796 · HTML: https://mlsanity.com/paper/1806.03796/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [A Capsule Network for Hierarchical Multi-Label Image Classification](https://mlsanity.com/paper/2209.05723/) (2022)
- [Capsule networks with non-iterative cluster routing](https://mlsanity.com/paper/2109.09213/) (2021)
- [A Novel Capsule Neural Network Based Model for Drowsiness Detection Using Electroencephalography Signals](https://mlsanity.com/paper/2204.01666/) (2022)

## Abstract (arXiv)

In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based on the principal of positional-equivariance of features, Capsule Network's ability to encode spatial relationships between the features of the image helps it become a more powerful critic in comparison to Convolutional Neural Networks (CNNs) used in current architectures for image synthesis. Our proposed GAN architectures learn the data manifold much faster and therefore, synthesize visually accurate images in significantly lesser number of training samples and training epochs in comparison to GANs and its variants that use CNNs. Apart from analyzing the quantitative results corresponding the images generated by different architectures, we also explore the reasons for the lower coverage and diversity explored by the GAN architectures that use CNN critics.