  Generative Adversarial Networks (GANs) are able to generate high-quality
images, but it remains difficult to explicitly specify the semantics of
synthesized images. In this work, we aim to better understand the semantic
representation of GANs, and thereby enable semantic control in GAN's generation
process. Interestingly, we find that a well-trained GAN encodes image semantics
in its internal feature maps in a surprisingly simple way: a linear
transformation of feature maps suffices to extract the generated image
semantics. To verify this simplicity, we conduct extensive experiments on
various GANs and datasets; and thanks to this simplicity, we are able to learn
a semantic segmentation model for a trained GAN from a small number (e.g., 8)
of labeled images. Last but not least, leveraging our findings, we propose two
few-shot image editing approaches, namely Semantic-Conditional Sampling and
Semantic Image Editing. Given a trained GAN and as few as eight semantic
annotations, the user is able to generate diverse images subject to a
user-provided semantic layout, and control the synthesized image semantics. We
have made the code publicly available.
