  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.
