  Learning disentangled representations of data is a fundamental problem in
artificial intelligence. Specifically, disentangled latent representations
allow generative models to control and compose the disentangled factors in the
synthesis process. Current methods, however, require extensive supervision and
training, or instead, noticeably compromise quality. In this paper, we present
a method that learns how to represent data in a disentangled way, with minimal
supervision, manifested solely using available pre-trained networks. Our key
insight is to decouple the processes of disentanglement and synthesis, by
employing a leading pre-trained unconditional image generator, such as
StyleGAN. By learning to map into its latent space, we leverage both its
state-of-the-art quality, and its rich and expressive latent space, without the
burden of training it. We demonstrate our approach on the complex and high
dimensional domain of human heads. We evaluate our method qualitatively and
quantitatively, and exhibit its success with de-identification operations and
with temporal identity coherency in image sequences. Through extensive
experimentation, we show that our method successfully disentangles identity
from other facial attributes, surpassing existing methods, even though they
require more training and supervision.
