  Generative adversarial networks (GANs) synthesize realistic images from a
random latent vector. While many studies have explored various training
configurations and architectures for GANs, the problem of inverting a
generative model to extract latent vectors of given input images has been
inadequately investigated. Although there is exactly one generated image per
given random vector, the mapping from an image to its recovered latent vector
can have more than one solution. We train a ResNet architecture to recover a
latent vector for a given face that can be used to generate a face nearly
identical to the target. We use a perceptual loss to embed face details in the
recovered latent vector while maintaining visual quality using a pixel loss.
The vast majority of studies on latent vector recovery perform well only on
generated images, we argue that our method can be used to determine a mapping
between real human faces and latent-space vectors that contain most of the
important face style details. In addition, our proposed method projects
generated faces to their latent-space with high fidelity and speed. At last, we
demonstrate the performance of our approach on both real and generated faces.
