Improving Neural Network Generalization by Combining Parallel Circuits with Dropout
Abstract
In an attempt to solve the lengthy training times of neural networks, we proposed Parallel Circuits (PCs), a biologically inspired architecture. Previous work has shown that this approach fails to maintain generalization performance in spite of achieving sharp speed gains. To address this issue, and motivated by the way Dropout prevents node co-adaption, in this paper, we suggest an improvement by extending Dropout to the PC architecture. The paper provides multiple insights into this combination, including a variety of fusion approaches. Experiments show promising results in which improved error rates are achieved in most cases, whilst maintaining the speed advantage of the PC approach.
Cite
@article{arxiv.1612.04970,
title = {Improving Neural Network Generalization by Combining Parallel Circuits with Dropout},
author = {Kien Tuong Phan and Tomas Henrique Maul and Tuong Thuy Vu and Lai Weng Kin},
journal= {arXiv preprint arXiv:1612.04970},
year = {2016}
}
Comments
Pre-print. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46675-0_63