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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'shigh-level instructions, with good qualitative and quantitative performance. Tooptimize for feature disentanglement, we also investigate two different latentspace that could be traversed for modification. The problem of mode collapse ischaracterized in detail for model robustness. This work could be easilyextended to content-aware image editor based on other GANs and provide insighton mode collapse problems in more general settings.

TunaGAN: Interpretable GAN for Smart Editing