Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes from real data or is produced by G. Under such a formulation, D plays as the rule maker and hence tends to dominate the competition. Towards a fairer game in GANs, we propose a new paradigm for adversarial training, which makes G assign a task to D as well. Specifically, given an image, we expect D to extract representative features that can be adequately decoded by G to reconstruct the input. That way, instead of learning freely, D is urged to align with the view of G for domain classification. Experimental results on various datasets demonstrate the substantial superiority of our approach over the baselines. For instance, we improve the FID of StyleGAN2 from 4.30 to 2.55 on LSUN Bedroom and from 4.04 to 2.82 on LSUN Church. We believe that the pioneering attempt present in this work could inspire the community with better designed generator-leading tasks for GAN improvement.

翻译：生成对抗网络（GAN）被形式化为生成器（G）与判别器（D）之间的双人博弈，其中D被要求区分图像来自真实数据还是由G生成。在这种框架下，D扮演规则制定者的角色，因此倾向于主导竞争。为实现GAN中更公平的博弈，我们提出了一种新的对抗训练范式，使G也能为D分配任务。具体而言，给定一张图像，我们期望D提取可被G充分解码以重建输入的代表性特征。通过这种方式，D不再自由学习，而是被迫与G的视角对齐以进行域分类。在各数据集上的实验结果表明，我们的方法显著优于基线方法。例如，我们在LSUN Bedroom上将StyleGAN2的FID从4.30提升至2.55，在LSUN Church上则从4.04提升至2.82。我们相信，本工作中的开创性尝试能够启发学界设计更优的生成器主导型任务以改进生成对抗网络。