  The GANs promote an adversarive game to approximate complex and jointed
example probability. The networks driven by noise generate fake examples to
approximate realistic data distributions. Later the conditional GAN merges
prior-conditions as input in order to transfer attribute vectors to the
corresponding data. However, the CGAN is not designed to deal with the high
dimension conditions since indirect guide of the learning is inefficiency. In
this paper, we proposed a network ResGAN to generate fine images in terms of
extremely degenerated images. The coarse images aligned to attributes are
embedded as the generator inputs and classifier labels. In generative network,
a straight path similar to the Resnet is cohered to directly transfer the
coarse images to the higher layers. And adversarial training is circularly
implemented to prevent degeneration of the generated images. Experimental
results of applying the ResGAN to datasets MNIST, CIFAR10/100 and CELEBA show
its higher accuracy to the state-of-art GANs.
