  Recent advances in Generative Adversarial Networks (GANs) have led to their
widespread adoption for the purposes of generating high quality synthetic
imagery. While capable of generating photo-realistic images, these models often
produce unrealistic samples which fall outside of the data manifold. Several
recently proposed techniques attempt to avoid spurious samples, either by
rejecting them after generation, or by truncating the model's latent space.
While effective, these methods are inefficient, as a large fraction of training
time and model capacity are dedicated towards samples that will ultimately go
unused. In this work we propose a novel approach to improve sample quality:
altering the training dataset via instance selection before model training has
taken place. By refining the empirical data distribution before training, we
redirect model capacity towards high-density regions, which ultimately improves
sample fidelity, lowers model capacity requirements, and significantly reduces
training time. Code is available at
https://github.com/uoguelph-mlrg/instance_selection_for_gans.
