  We investigate factors controlling DNN diversity in the context of the Google
Cloud and YouTube-8M Video Understanding Challenge. While it is well-known that
ensemble methods improve prediction performance, and that combining accurate
but diverse predictors helps, there is little knowledge on how to best promote
& measure DNN diversity. We show that diversity can be cultivated by some
unexpected means, such as model over-fitting or dropout variations. We also
present details of our solution to the video understanding problem, which
ranked #7 in the Kaggle competition (competing as the Yeti team).
