SPA-GAN: Spatial Attention GAN for Image-to-Image Translation

Understand

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.