Abstract:
Convolutional neural network based generative adversarial networks have become the
dominant generative model in the field of generative deep learning. But limitations of
convolutional neural networks affect generative adversarial networks also, since most of
the current generative adversarial networks are based on convolutional neural networks.
The main limitation of convolutional neural networks is that they are invariant. In other
words, convolutional neural networks can’t preserve spatial information of features in an
image. In contrast, capsule networks gained attention in recent years due to their
equivariant architecture which preserves spatial information.
Stacked capsule autoencoder is a type of capsule networks that is able to overcome the
limitations that convolutional neural networks suffer from. Stacked capsule autoencoder
is an equivariant model which preserves spatial, relational, geometrical information
between parts and objects in an image. So in this research we implemented a generative
adversarial network which uses stacked capsule autoencoder as the discriminator of it,
by replacing the conventional convolutional neural network discriminator.
Then we evaluated the implementation of stacked capsule autoencoder based generative
adversarial network using MNIST images. As the qualitative evaluation we observed the
visual quality of generated images. Quality and diversity of the generated images are
acceptable. Then we evaluated our model quantitatively using inception score for
MNIST. Findings of this research show that, the stacked capsule autoencoder can be
used as the discriminator of a generative adversarial network instead a convolutional
neural network and its performances are plausible.
Citation:
Madhusanka, G.A.C. (2020). Stacked capsule autoencoder based generative adversarial network [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21205