  In this preregistration submission, we propose an empirical study of how
networks handle changes in complexity of the data. We investigate the effect of
network capacity on generalization performance in the face of increasing data
complexity. For this, we measure the generalization error for an image
classification task where the number of classes steadily increases. We compare
a number of modern architectures at different scales in this setting. The
methodology, setup, and hypotheses described in this proposal were evaluated by
peer review before experiments were conducted.
