  Although a plethora of architectural variants for deep classification has
been introduced over time, recent works have found empirical evidence towards
similarities in their training process. It has been hypothesized that neural
networks converge not only to similar representations, but also exhibit a
notion of empirical agreement on which data instances are learned first.
Following in the latter works$'$ footsteps, we define a metric to quantify the
relationship between such classification agreement over time, and posit that
the agreement phenomenon can be mapped to core statistics of the investigated
dataset. We empirically corroborate this hypothesis across the CIFAR10, Pascal,
ImageNet and KTH-TIPS2 datasets. Our findings indicate that agreement seems to
be independent of specific architectures, training hyper-parameters or labels,
albeit follows an ordering according to image statistics.
