论文标题
Montagegan：通过gans生成和组装多个组件
MontageGAN: Generation and Assembly of Multiple Components by GANs
论文作者
论文摘要
从图形设计师的角度来看，多层图像比单层图像更有价值。但是，到目前为止，大多数提出的图像生成方法都集中在单层图像上。在本文中，我们提出了Montagegan，这是一种生成对抗网络（GAN）框架，用于生成多层图像。我们的方法采用了一种两步方法，该方法包括当地甘斯和全球甘纳。每个本地gan都学会生成特定的图像层，并且全局gan学习了每个生成的图像层的位置。通过我们的实验，我们展示了方法生成多层图像并估算生成图像层的位置的能力。
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.
