  Recent research has shown that numerous human-interpretable directions exist
in the latent space of GANs. In this paper, we develop an automatic procedure
for finding directions that lead to foreground-background image separation, and
we use these directions to train an image segmentation model without human
supervision. Our method is generator-agnostic, producing strong segmentation
results with a wide range of different GAN architectures. Furthermore, by
leveraging GANs pretrained on large datasets such as ImageNet, we are able to
segment images from a range of domains without further training or finetuning.
Evaluating our method on image segmentation benchmarks, we compare favorably to
prior work while using neither human supervision nor access to the training
data. Broadly, our results demonstrate that automatically extracting
foreground-background structure from pretrained deep generative models can
serve as a remarkably effective substitute for human supervision.
