  Generative adversarial networks (GANs) have proven to be surprisingly
efficient for image editing by inverting and manipulating the latent code
corresponding to a natural image. This property emerges from the disentangled
nature of the latent space. In this paper, we identify two geometric
limitations of such latent space: (a) euclidean distances differ from image
perceptual distance, and (b) disentanglement is not optimal and facial
attribute separation using linear model is a limiting hypothesis. We thus
propose a new method to learn a proxy latent representation using normalizing
flows to remedy these limitations, and show that this leads to a more efficient
space for face image editing.
