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

Generator Knows What Discriminator Should Learn in Unconditional GANs