Robustness, Stability and Performance of Optimization Algorithms for GAN Training
Training Generative Adversial Networks (GANs) for image synthesis problems is a largely heuristical process that is known to be fickle and difficult to set up reliably. To avoid common failure modes and succeed with GAN training, one needs to find very specific hyperparameter settings carefully tuned to the model architectures and datasets at hand. Standard GAN training optimization methods such a
