  Generative Adversarial Networks (GANs) have witnessed significant advances in
recent years, generating increasingly higher quality images, which are
non-distinguishable from real ones. Recent GANs have proven to encode features
in a disentangled latent space, enabling precise control over various semantic
attributes of the generated facial images such as pose, illumination, or
gender. GAN inversion, which is projecting images into the latent space of a
GAN, opens the door for the manipulation of facial semantics of real face
images. This is useful for numerous applications such as evaluating the
performance of face recognition systems. In this work, EGAIN, an architecture
for constructing GAN inversion models, is presented. This architecture
explicitly addresses some of the shortcomings in previous GAN inversion models.
A specific model with the same name, egain, based on this architecture is also
proposed, demonstrating superior reconstruction quality over state-of-the-art
models, and illustrating the validity of the EGAIN architecture.
