Dropout as data augmentation
Machine Learning
2016-01-11 v4 Machine Learning
Abstract
Dropout is typically interpreted as bagging a large number of models sharing parameters. We show that using dropout in a network can also be interpreted as a kind of data augmentation in the input space without domain knowledge. We present an approach to projecting the dropout noise within a network back into the input space, thereby generating augmented versions of the training data, and we show that training a deterministic network on the augmented samples yields similar results. Finally, we propose a new dropout noise scheme based on our observations and show that it improves dropout results without adding significant computational cost.
Cite
@article{arxiv.1506.08700,
title = {Dropout as data augmentation},
author = {Xavier Bouthillier and Kishore Konda and Pascal Vincent and Roland Memisevic},
journal= {arXiv preprint arXiv:1506.08700},
year = {2016}
}