Deep learning approaches heavily rely on high-quality human supervision which
is nonetheless expensive, time-consuming, and error-prone, especially for image
segmentation task. In this paper, we propose a method to automatically
synthesize paired photo-realistic images and segmentation masks for the use of
training a foreground-background segmentation network. In particular, we learn
a generative adversarial network that decomposes an image into foreground and
background layers, and avoid trivial decompositions by maximizing mutual
information between generated images and latent variables. The improved layered
GANs can synthesize higher quality datasets from which segmentation networks of
higher performance can be learned. Moreover, the segmentation networks are
employed to stabilize the training of layered GANs in return, which are further
alternately trained with Layered GANs. Experiments on a variety of
single-object datasets show that our method achieves competitive generation
quality and segmentation performance compared to related methods.

提出了一种自动合成图像和分割掩模的方法，利用生成式对抗网络学习将图像分解成前景和背景层，生成高质量数据集并用于训练前景 - 背景分割网络，同时使用前景背景分割网络稳定分层 GAN 的训练。实验证明其生成质量和分割性能与相关方法相似。