  Generative adversarial networks (GANs) have demonstrated to be successful at
generating realistic real-world images. In this paper we compare various GAN
techniques, both supervised and unsupervised. The effects on training stability
of different objective functions are compared. We add an encoder to the
network, making it possible to encode images to the latent space of the GAN.
The generator, discriminator and encoder are parameterized by deep
convolutional neural networks. For the discriminator network we experimented
with using the novel Capsule Network, a state-of-the-art technique for
detecting global features in images. Experiments are performed using a digit
and face dataset, with various visualizations illustrating the results. The
results show that using the encoder network it is possible to reconstruct
images. With the conditional GAN we can alter visual attributes of generated or
encoded images. The experiments with the Capsule Network as discriminator
result in generated images of a lower quality, compared to a standard
convolutional neural network.
