  With a goal of understanding what drives generalization in deep networks, we
consider several recently suggested explanations, including norm-based control,
sharpness and robustness. We study how these measures can ensure
generalization, highlighting the importance of scale normalization, and making
a connection between sharpness and PAC-Bayes theory. We then investigate how
well the measures explain different observed phenomena.
