Finding an Unsupervised Image Segmenter in Each of Your Deep Generative Models

Understand

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.