  Recently, Conditional Generative Adversarial Network (Conditional GAN) have
shown very promising performance in several image-to-image translation
applications. However, the uses of these conditional GANs are quite limited to
low-resolution images, such as 256X256.The Pix2Pix-HD is a recent attempt to
utilize the conditional GAN for high-resolution image synthesis. In this paper,
we propose a Multi-Scale Gradient based U-Net (MSG U-Net) model for
high-resolution image-to-image translation up to 2048X1024 resolution. The
proposed model is trained by allowing the flow of gradients from
multiple-discriminators to a single generator at multiple scales. The proposed
MSG U-Net architecture leads to photo-realistic high-resolution image-to-image
translation. Moreover, the proposed model is computationally efficient as
com-pared to the Pix2Pix-HD with an improvement in the inference time nearly by
2.5 times. We provide the code of MSG U-Net model at
https://github.com/laxmaniron/MSG-U-Net.
