  The field of Deep Learning is rich with empirical evidence of human-like
performance on a variety of regression, classification, and control tasks.
However, despite these successes, the field lacks strong theoretical error
bounds and consistent measures of network generalization and learned
invariances. In this work, we introduce two new measures, the Gi-score and
Pal-score, that capture a deep neural network's generalization capabilities.
Inspired by the Gini coefficient and Palma ratio, measures of income
inequality, our statistics are robust measures of a network's invariance to
perturbations that accurately predict generalization gaps, i.e., the difference
between accuracy on training and test sets.
