  A multi-layer image is more valuable than a single-layer image from a graphic
designer's perspective. However, most of the proposed image generation methods
so far focus on single-layer images. In this paper, we propose MontageGAN,
which is a Generative Adversarial Networks (GAN) framework for generating
multi-layer images. Our method utilized a two-step approach consisting of local
GANs and global GAN. Each local GAN learns to generate a specific image layer,
and the global GAN learns the placement of each generated image layer. Through
our experiments, we show the ability of our method to generate multi-layer
images and estimate the placement of the generated image layers.
