  Generating images via the generative adversarial network (GAN) has attracted
much attention recently. However, most of the existing GAN-based methods can
only produce low-resolution images of limited quality. Directly generating
high-resolution images using GANs is nontrivial, and often produces problematic
images with incomplete objects. To address this issue, we develop a novel GAN
called Auto-Embedding Generative Adversarial Network (AEGAN), which
simultaneously encodes the global structure features and captures the
fine-grained details. In our network, we use an autoencoder to learn the
intrinsic high-level structure of real images and design a novel denoiser
network to provide photo-realistic details for the generated images. In the
experiments, we are able to produce 512x512 images of promising quality
directly from the input noise. The resultant images exhibit better perceptual
photo-realism, i.e., with sharper structure and richer details, than other
baselines on several datasets, including Oxford-102 Flowers, Caltech-UCSD Birds
(CUB), High-Quality Large-scale CelebFaces Attributes (CelebA-HQ), Large-scale
Scene Understanding (LSUN) and ImageNet.
