Interleaving GANs with knowledge graphs to support design creativity for book covers
This is an incremental improvement for book cover design, aiding authors or editors in creative processes.
The paper tackled generating book covers by interleaving GANs with knowledge graphs to augment input titles, resulting in better generated images and improved options for authors or editors compared to using GANs alone.
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.