  Recent methods for conditional image generation benefit from dense
supervision such as segmentation label maps to achieve high-fidelity. However,
it is rarely explored to employ dense supervision for unconditional image
generation. Here we explore the efficacy of dense supervision in unconditional
generation and find generator feature maps can be an alternative of
cost-expensive semantic label maps. From our empirical evidences, we propose a
new generator-guided discriminator regularization(GGDR) in which the generator
feature maps supervise the discriminator to have rich semantic representations
in unconditional generation. In specific, we employ an U-Net architecture for
discriminator, which is trained to predict the generator feature maps given
fake images as inputs. Extensive experiments on mulitple datasets show that our
GGDR consistently improves the performance of baseline methods in terms of
quantitative and qualitative aspects. Code is available at
https://github.com/naver-ai/GGDR
