  The recent success of Generative Adversarial Networks (GAN) is a result of
their ability to generate high quality images from a latent vector space. An
important application is the generation of images from a text description,
where the text description is encoded and further used in the conditioning of
the generated image. Thus the generative network has to additionally learn a
mapping from the text latent vector space to a highly complex and multi-modal
image data distribution, which makes the training of such models challenging.
To handle the complexities of fashion image and meta data, we propose Ontology
Generative Adversarial Networks (O-GANs) for fashion image synthesis that is
conditioned on an hierarchical fashion ontology in order to improve the image
generation fidelity. We show that the incorporation of the ontology leads to
better image quality as measured by Fr\'{e}chet Inception Distance and
Inception Score. Additionally, we show that the O-GAN achieves better
conditioning results evaluated by implicit similarity between the text and the
generated image.

Machine Learning;Machine Learning