  Although Generative Adversarial Networks have shown remarkable performance in
image generation, there are some challenges in image realism and convergence
speed. The results of some models display the imbalances of quality within a
generated image, in which some defective parts appear compared with other
regions. Different from general single global optimization methods, we
introduce an adaptive global and local bilevel optimization model(GL-GAN). The
model achieves the generation of high-resolution images in a complementary and
promoting way, where global optimization is to optimize the whole images and
local is only to optimize the low-quality areas. With a simple network
structure, GL-GAN is allowed to effectively avoid the nature of imbalance by
local bilevel optimization, which is accomplished by first locating low-quality
areas and then optimizing them. Moreover, by using feature map cues from
discriminator output, we propose the adaptive local and global optimization
method(Ada-OP) for specific implementation and find that it boosts the
convergence speed. Compared with the current GAN methods, our model has shown
impressive performance on CelebA, CelebA-HQ and LSUN datasets.
