  Generative adversarial nets (GANs) have been remarkably successful at
learning to sample from distributions specified by a given dataset,
particularly if the given dataset is reasonably large compared to its
dimensionality. However, given limited data, classical GANs have struggled, and
strategies like output-regularization, data-augmentation, use of pre-trained
models and pruning have been shown to lead to improvements. Notably, the
applicability of these strategies is 1) often constrained to particular
settings, e.g., availability of a pretrained GAN; or 2) increases training
time, e.g., when using pruning. In contrast, we propose a Discriminator
gradIent Gap regularized GAN (DigGAN) formulation which can be added to any
existing GAN. DigGAN augments existing GANs by encouraging to narrow the gap
between the norm of the gradient of a discriminator's prediction w.r.t.\ real
images and w.r.t.\ the generated samples. We observe this formulation to avoid
bad attractors within the GAN loss landscape, and we find DigGAN to
significantly improve the results of GAN training when limited data is
available. Code is available at \url{https://github.com/AilsaF/DigGAN}.
