Generator Knows What Discriminator Should Learn in Unconditional GANs. (arXiv:2207.13320v1 [cs.CV])

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 …

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