Mosca, Alan and Magoulas, George D. (2017) Boosted Residual Networks. In: Boracchi, G. and Iliadis, L. and Jayne, C. and Likas, A. (eds.) Engineering Applications of Neural Networks. Communications in Computer and Information Science 744. Springer, pp. 137-148. ISBN 9783319651712.
Abstract
In this paper we present a new ensemble method, called Boosted Residual Networks, which builds an ensemble of Residual Networks by growing the member network at each round of boosting. The proposed approach combines recent developements in Residual Networks - a method for creating very deep networks by including a shortcut layer between different groups of layers - with the Deep Incremental Boosting, which has been proposed as a methodology to train fast ensembles of networks of increasing depth through the use of boosting. We demonstrate that the synergy of Residual Networks and Deep Incremental Boosting has better potential than simply boosting a Residual Network of fixed structure or using the equivalent Deep Incremental Boosting without the shortcut layers.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | Series Print ISSN: 1865-0929 |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Administrator |
| Date Deposited: | 02 Jul 2018 09:52 |
| Last Modified: | 09 Aug 2023 12:42 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/20191 |
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