  Generative Adversarial Network (GAN) is widely adopted in numerous
application areas, such as data preprocessing, image editing, and creativity
support. However, GAN's 'black box' nature prevents non-expert users from
controlling what data a model generates, spawning a plethora of prior work that
focused on algorithm-driven approaches to extract editing directions to control
GAN. Complementarily, we propose a GANzilla: a user-driven tool that empowers a
user with the classic scatter/gather technique to iteratively discover
directions to meet their editing goals. In a study with 12 participants,
GANzilla users were able to discover directions that (i) edited images to match
provided examples (closed-ended tasks) and that (ii) met a high-level goal,
e.g., making the face happier, while showing diversity across individuals
(open-ended tasks).
