Image-to-image translation has been made much progress with embracing
Generative Adversarial Networks (GANs). However, it's still very challenging
for translation tasks that require high quality, especially at high-resolution
and photorealism. In this paper, we present Discriminative Region Proposal
Adversarial Networks (DRPAN) for high-quality image-to-image translation. We
decompose the procedure of image-to-image translation task into three iterated
steps, first is to generate an image with global structure but some local
artifacts (via GAN), second is using our DRPnet to propose the most fake region
from the generated image, and third is to implement "image inpainting" on the
most fake region for more realistic result through a reviser, so that the
system (DRPAN) can be gradually optimized to synthesize images with more
attention on the most artifact local part. Experiments on a variety of
image-to-image translation tasks and datasets validate that our method
outperforms state-of-the-arts for producing high-quality translation results in
terms of both human perceptual studies and automatic quantitative measures.

本文介绍了一种基于 DRPAN 的高质量图像到图像翻译模型，该模型将图像到图像翻译任务分解为三个步骤：生成一个具有全局结构但含有某些局部伪迹的图像（通过 GAN），然后使用 DRPnet 提出图像中最伪造的区域，并在最伪造的区域上通过修正器实现图像修补，从而逐步优化系统以生成更高质量的翻译结果。实验表明，该方法在各种图像到图像翻译任务和数据集上优于现有技术，并能以人类感知研究和自动量化度量等方面提供高质量的翻译结果。