Recently, Generative Adversarial Network (GAN) and numbers of its variants have been widely used to solve the image-to-image translation problem and achieved extraordinary results in both a supervised and unsupervised manner. However, most GAN-based methods suffer from the imbalance problem between the generator and discriminator in practice. Namely, the relative model capacities of the generator and discriminator do not match, leading to mode collapse and/or diminished gradients. To tackle this problem, we propose a GuideGAN based on attention mechanism. More specifically, we arm the discriminator with an attention mechanism so not only it estimates the probability that its input is real, but also does it create an attention map that highlights the critical features for such prediction. This attention map then assists the generator to produce more plausible and realistic images. We extensively evaluate the proposed GuideGAN framework on a  number of image transfer tasks. Both qualitative results and quantitative comparison demonstrate the superiority of our proposed approach.
A general method that improves the image translation performance of GAN framework by using an attention embedded discriminator A feedback mechanism in the GAN framework which improves the quality of generated images in image-to-image translation, and whose discriminator outputs a map indicating where the generator should focus to make its results more convincing. Proposal for a GAN with an attention-based discriminator for I2I translation which provides the probability of real/fake and an attention map which reflects salience for image generation.