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The success of Generative Adversarial Networks (GANs) is largely built uponthe adversarial training between a generator (G) and a discriminator (D). Theyare expected to reach a certain equilibrium where D cannot distinguish thegenerated images from the real ones. However, such an equilibrium is rarelyachieved in practical GAN training, instead, D almost always surpasses G. Weattribute one of its sources to the information asymmetry between D and G. Weobserve that D learns its own visual attention when determining whether animage is real or fake, but G has no explicit clue on which regions to focus onfor a particular synthesis. To alleviate the issue of D dominating thecompetition in GANs, we aim to raise the spatial awareness of G. Randomlysampled multi-level heatmaps are encoded into the intermediate layers of G asan inductive bias. Thus G can purposefully improve the synthesis of certainimage regions. We further propose to align the spatial awareness of G with theattention map induced from D. Through this way we effectively lessen theinformation gap between D and G. Extensive results show that our method pushesthe two-player game in GANs closer to the equilibrium, leading to a bettersynthesis performance. As a byproduct, the introduced spatial awarenessfacilitates interactive editing over the output synthesis. Demo video and codeare available at https://genforce.github.io/eqgan-sa/.

Improving GAN Equilibrium by Raising Spatial Awareness