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

Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization