  Deep generative models are becoming a cornerstone of modern machine learning.
Recent work on conditional generative adversarial networks has shown that
learning complex, high-dimensional distributions over natural images is within
reach. While the latest models are able to generate high-fidelity, diverse
natural images at high resolution, they rely on a vast quantity of labeled
data. In this work we demonstrate how one can benefit from recent work on self-
and semi-supervised learning to outperform the state of the art on both
unsupervised ImageNet synthesis, as well as in the conditional setting. In
particular, the proposed approach is able to match the sample quality (as
measured by FID) of the current state-of-the-art conditional model BigGAN on
ImageNet using only 10% of the labels and outperform it using 20% of the
labels.
