  Image generation remains a fundamental problem in artificial intelligence in
general and deep learning in specific. The generative adversarial network (GAN)
was successful in generating high quality samples of natural images. We propose
a model called composite generative adversarial network, that reveals the
complex structure of images with multiple generators in which each generator
generates some part of the image. Those parts are combined by alpha blending
process to create a new single image. It can generate, for example, background
and face sequentially with two generators, after training on face dataset.
Training was done in an unsupervised way without any labels about what each
generator should generate. We found possibilities of learning the structure by
using this generative model empirically.
