  Generative adversarial networks (GANs) have many application areas including
image editing, domain translation, missing data imputation, and support for
creative work. However, GANs are considered 'black boxes'. Specifically, the
end-users have little control over how to improve editing directions through
disentanglement. Prior work focused on new GAN architectures to disentangle
editing directions. Alternatively, we propose GANravel a user-driven direction
disentanglement tool that complements the existing GAN architectures and allows
users to improve editing directions iteratively. In two user studies with 16
participants each, GANravel users were able to disentangle directions and
outperformed the state-of-the-art direction discovery baselines in
disentanglement performance. In the second user study, GANravel was used in a
creative task of creating dog memes and was able to create high-quality edited
images and GIFs.
