EqGAN - Improving GAN Equilibrium by Raising Spatial Awareness

Improving GAN Equilibrium by Raising Spatial Awareness
Jianyuan Wang, Ceyuan Yang, Yinghao Xu, Yujun Shen, Hongdong Li, Bolei Zhou
arXiv preprint

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In Generative Adversarial Networks (GANs), a generator (G) and a discriminator (D) are expected to reach a certain equilibrium where D cannot distinguish the generated images from the real ones. However, in practice it is difficult to achieve such an equilibrium in GAN training, instead, D almost always surpasses G. We attribute this phenomenon to the information asymmetry that D learns its own visual attention when determining whether an image is real or fake, but G has no explicit clue on which regions to focus on.

To alleviate the issue of D dominating the competition in GANs, we aim to raise the spatial awareness of G. We encode randomly sampled multi-level heatmaps into the intermediate layers of G as an inductive bias. We further propose to align the spatial awareness of G with the attention map induced from D. Through this way we effectively lessen the information gap between D and G. Extensive results show that our method pushes the two-player game in GANs closer to the equilibrium, leading to a better synthesis performance. As a byproduct, the introduced spatial awareness facilitates interactive editing over the output synthesis.

BibTeX

@article{wang2021eqgan,
  title   = {Improving GAN Equilibrium by Raising Spatial Awareness},
  author  = {Wang, Jianyuan and Yang, Ceyuan and Xu, Yinghao and Shen, Yujun and Li, Hongdong and Zhou, Bolei},
  article = {arXiv preprint},
  year    = {2021}
}