  In this paper, we tackle a challenging domain conversion task between photo
and icon images. Although icons often originate from real object images (i.e.,
photographs), severe abstractions and simplifications are applied to generate
icon images by professional graphic designers. Moreover, there is no one-to-one
correspondence between the two domains, for this reason we cannot use it as the
ground-truth for learning a direct conversion function. Since generative
adversarial networks (GAN) can undertake the problem of domain conversion
without any correspondence, we test CycleGAN and UNIT to generate icons from
objects segmented from photo images. Our experiments with several image
datasets prove that CycleGAN learns sufficient abstraction and simplification
ability to generate icon-like images.
