# Cultivating DNN Diversity for Large Scale Video Labelling

arXiv: https://arxiv.org/abs/1707.04272 · HTML: https://mlsanity.com/paper/1707.04272/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification](https://mlsanity.com/paper/2408.14441/) (2024)

## Abstract (arXiv)

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).