  Training deep networks with limited labeled data while achieving a strong
generalization ability is key in the quest to reduce human annotation efforts.
This is the goal of semi-supervised learning, which exploits more widely
available unlabeled data to complement small labeled data sets. In this paper,
we propose a novel framework for discriminative pixel-level tasks using a
generative model of both images and labels. Concretely, we learn a generative
adversarial network that captures the joint image-label distribution and is
trained efficiently using a large set of unlabeled images supplemented with
only few labeled ones. We build our architecture on top of StyleGAN2, augmented
with a label synthesis branch. Image labeling at test time is achieved by first
embedding the target image into the joint latent space via an encoder network
and test-time optimization, and then generating the label from the inferred
embedding. We evaluate our approach in two important domains: medical image
segmentation and part-based face segmentation. We demonstrate strong in-domain
performance compared to several baselines, and are the first to showcase
extreme out-of-domain generalization, such as transferring from CT to MRI in
medical imaging, and photographs of real faces to paintings, sculptures, and
even cartoons and animal faces. Project Page:
\url{https://nv-tlabs.github.io/semanticGAN/}
