On conditioning GANs to hierarchical ontologies

H. Eghbal-zadeh, L. Fischer, T. Hoch. On conditioning GANs to hierarchical ontologies. volume 1062, pages 182-186, DOI 10.1007/978-3-030-27684-3_23, 8, 2019.

Autoren
  • Hamid Eghbal-zadeh
  • Lukas Fischer
  • Thomas Hoch
Editoren
  • G. Anderst-Kotsis
  • A Min Tjoa
  • I. Khalil
  • et al.
BuchDatabase and Expert Systems Applications - Proc DEXA 209 International Workshops
TypIn Konferenzband
VerlagSpringer
SerieCommunications in Computer and Information Science
Band1062
DOI10.1007/978-3-030-27684-3_23
ISBN978-3-030-27683-6
Monat8
Jahr2019
Seiten182-186
Abstract

The recent success of Generative Adversarial Networks (GAN) is a result of their ability to generate high quality images given samples from a latent space. One of the applications of GANs is to generate images from a text description, where the text is first encoded and further used for the conditioning in the generative model. In addition to text, conditional generative models often use label information for conditioning. Hence, the structure of the meta-data and the ontology of the labels is important for such models. In this paper, we propose Ontology Generative Adversarial Networks (O-GANs) to handle the complexities of the data with label ontology. We evaluate our model on a dataset of fashion images with hierarchical label structure. Our results suggest that the incorporation of the ontology, leads to better image quality as measured by Fréchet Inception Distance and Inception Score. Additionally, we show that the O-GAN better matches the generated images to their conditioning text, compared to models that do not incorporate the label ontology.