TunaGAN: Interpretable GAN for Smart Editing

16 Aug 2019Weiquan MaoBeicheng LouJiyao Yuan

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... (read more)

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