  Adversarial generative model have successfully manifest itself in image
synthesis. However, the performance deteriorate and unstable, because
discriminator is far stable than generator, and it is hard to control the game
between the two modules. Various methods have been introduced to tackle the
problem such as WGAN, Relativistic GAN and their successors by adding or
restricting the loss function, which certainly help balance the min-max game,
but they all focused on the loss function ignoring the intrinsic structure
limitation. We present a UU-Net architecture inspired by U-net bridging the
encoder and the decoder, UU-Net composed by two U-Net liked modules
respectively served as generator and discriminator. Because the modules in
U-net are symmetrical, therefore it shares weights easily between all four
components. Thanks to UU-net's modules identical and symmetric property, we
could not only carried the features from inner generator's encoder to its
decoder, but also to the discriminator's encoder and decoder. By this design,
it give us more control and condition flexibility to intervene the process
between the generator and the discriminator.
