  Understating and controlling generative models' latent space is a complex
task.
  In this paper, we propose a novel method for learning to control any desired
attribute in a pre-trained GAN's latent space, for the purpose of editing
synthesized and real-world data samples accordingly.
  We perform Sim2Real learning, relying on minimal samples to achieve an
unlimited amount of continuous precise edits.
  We present an Autoencoder-based model that learns to encode the semantics of
changes between images as a basis for editing new samples later on, achieving
precise desired results - example shown in Fig. 1.
  While previous editing methods rely on a known structure of latent spaces
(e.g., linearity of some semantics in StyleGAN), our method inherently does not
require any structural constraints.
  We demonstrate our method in the domain of facial imagery: editing different
expressions, poses, and lighting attributes, achieving state-of-the-art
results.
