Before revision:  This reduces computational cost (giving up to a 1.87\times speedup) , and removes the truncation errors of the double adjoint required for gradient penalty, substantially improving several test metrics.
After revision:  This reduces computational cost (giving up to a 1.87\times speedup) , and removes the numerical truncation errors associated with gradient penalty. Altogether, we outperform the state-of-the-art by substantial margins, with respect to training speed, and with respect to classification, prediction, and MMD test metrics.