Interleaving GANs with knowledge graphs to support design creativity for
  book covers
Alexandru Motogna Adrian Groza
An attractive book cover is important for the success of a book. In this
paper, we apply Generative Adversarial Networks (GANs) to the book covers
domain, using different methods for training in order to obtain better
generated images. We interleave GANs with knowledge graphs to alter the input
title to obtain multiple possible options for any given title, which are then
used as an augmented input to the generator. Finally, we use the discriminator
obtained during the training phase to select the best images generated with new
titles. Our method performed better at generating book covers than previous
attempts, and the knowledge graph gives better options to the book author or
editor compared to using GANs alone.