  Image-to-image translation is to learn a mapping between images from a source
domain and images from a target domain. In this paper, we introduce the
attention mechanism directly to the generative adversarial network (GAN)
architecture and propose a novel spatial attention GAN model (SPA-GAN) for
image-to-image translation tasks. SPA-GAN computes the attention in its
discriminator and use it to help the generator focus more on the most
discriminative regions between the source and target domains, leading to more
realistic output images. We also find it helpful to introduce an additional
feature map loss in SPA-GAN training to preserve domain specific features
during translation. Compared with existing attention-guided GAN models, SPA-GAN
is a lightweight model that does not need additional attention networks or
supervision. Qualitative and quantitative comparison against state-of-the-art
methods on benchmark datasets demonstrates the superior performance of SPA-GAN.
