Attention Guided Unsupervised Image-to-Image Translation with Progressively Growing Strategy

Unsupervised image-to-image translation such as CycleGAN has received considerable attention in recent research. However, when handling large images, the quality of generated images are not in good quality. Progressive Growing GAN has proved that progressively growing of GANs could generate high pixels images. However, if we simply combine PG-method and CycleGAN, it must bring model collapse. In this paper, motivated from skip connection, we propose Progressive Growing CycleGAN (PG-Att-CycleGAN), which can stably grow the input size of both the generator and discriminator progressively from \(256\times 256\) to \(512\times 512\) and finally \(1024\times 1024\) using the weight \({\alpha }\). The whole process makes generated images clearer and stabilizes training of the network. In addition, our new generator and discriminator cannot only make the domain transfer more natural, but also increase the stability of training by using the attention block. Finally, through our model, we can process high scale images with good qualities. We use VGG16 network to evaluate domain transfer ability.