State-of-the-art methods in image-to-image translation are capable of
learning a mapping from a source domain to a target domain with unpaired image
data. Though the existing methods have achieved promising results, they still
produce visual artifacts, being able to translate low-level information but not
high-level semantics of input images. One possible reason is that generators do
not have the ability to perceive the most discriminative parts between the
source and target domains, thus making the generated images low quality. In
this paper, we propose a new Attention-Guided Generative Adversarial Networks
(AttentionGAN) for the unpaired image-to-image translation task. AttentionGAN
can identify the most discriminative foreground objects and minimize the change
of the background. The attention-guided generators in AttentionGAN are able to
produce attention masks, and then fuse the generation output with the attention
masks to obtain high-quality target images. Accordingly, we also design a novel
attention-guided discriminator which only considers attended regions. Extensive
experiments are conducted on several generative tasks with eight public
datasets, demonstrating that the proposed method is effective to generate
sharper and more realistic images compared with existing competitive models.
The code is available at this https URL

本文提出了一种新的基于注意力向导的生成对抗网络 (AttentionGAN)，通过生成注意力掩码来识别最具区别性的前景物体，并将生成的输出融合到注意力掩码中，从而获得高质量的目标图像，实验表明，相较于现有的竞争模型，该方法能够生成更鲜明和更现实的图像。