  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.
