  We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs)
algorithm, a novel training procedure for GANs to improve stability and
performance by using an adaptive hinge loss function. We estimate the
appropriate hinge loss margin with the expected energy of the target
distribution, and derive principled criteria for when to update the margin. We
prove that our method converges to its global optimum under certain
assumptions. Evaluated on the task of unsupervised image generation, the
proposed training procedure is simple yet robust on a diverse set of data, and
achieves qualitative and quantitative improvements compared to the
state-of-the-art.
