Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to an input real image. This editing property emerges from the disentangled nature of the latent space. In this paper, we identify that the facial attribute disentanglement is not optimal, thus facial editing relying on linear attribute separation is flawed. We thus propose to improve semantic disentanglement with supervision. Our method consists in learning a proxy latent representation using normalizing flows, and we show that this leads to a more efficient space for face image editing.

翻译：生成的对抗性网络(GANs)已证明通过反转和操纵与输入真实图像相对应的潜在代码对图像编辑来说是惊人的高效。 这种编辑属性产生于潜在空间的分解性质。 在本文中, 我们发现面部属性分解不是最佳的, 因此依赖线性属性分离的面部编辑存在缺陷 。 因此, 我们提议改进语义分解与监管的分解。 我们的方法是使用正常流来学习代理潜在表达方式, 并且我们证明这可以导致更高效的面部图像编辑空间 。