  While Generative Adversarial Networks (GANs) have seen huge successes in
image synthesis tasks, they are notoriously difficult to adapt to different
datasets, in part due to instability during training and sensitivity to
hyperparameters. One commonly accepted reason for this instability is that
gradients passing from the discriminator to the generator become uninformative
when there isn't enough overlap in the supports of the real and fake
distributions. In this work, we propose the Multi-Scale Gradient Generative
Adversarial Network (MSG-GAN), a simple but effective technique for addressing
this by allowing the flow of gradients from the discriminator to the generator
at multiple scales. This technique provides a stable approach for high
resolution image synthesis, and serves as an alternative to the commonly used
progressive growing technique. We show that MSG-GAN converges stably on a
variety of image datasets of different sizes, resolutions and domains, as well
as different types of loss functions and architectures, all with the same set
of fixed hyperparameters. When compared to state-of-the-art GANs, our approach
matches or exceeds the performance in most of the cases we tried.
