Abstract:
The aim of image-to-image translation algorithms is to tackle the challenges of learning a proper mapping function across different domains. Generative Adversarial Networks (GAN) have shown superior ability to handle this problem by both supervised and unsupervised ways. However, one critical problem of GAN in practice is that the discriminator is typically much stronger than the generator, which could lead to failures such as mode collapse, diminished gradient, etc. To address these shortcomings, we propose a novel framework, which incorporates a powerful spatial attention mechanism to guide the generator. Specifically, our designed discriminator estimates the probability of realness of a given image, and provides an attention map regarding this prediction. The generated attention map contains the informative regions to distinguish the real and fake image, from the perspective of the discriminator. Such information is particularly valuable for the translation because the generator is encouraged to focus on those areas and produce more realistic images. We conduct extensive experiments and evaluations, and show that our proposed method is both qualitatively and quantitatively better than other state-of-the-art image translation frameworks.
