  In this paper, we introduce a tunable generative adversary network (TunaGAN)
that uses an auxiliary network on top of existing generator networks
(Style-GAN) to modify high-resolution face images according to user's
high-level instructions, with good qualitative and quantitative performance. To
optimize for feature disentanglement, we also investigate two different latent
space that could be traversed for modification. The problem of mode collapse is
characterized in detail for model robustness. This work could be easily
extended to content-aware image editor based on other GANs and provide insight
on mode collapse problems in more general settings.
