Title: Boosted Residual Networks
Authors: Alan Mosca, George D. Magoulas
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.
Venue: Submitted to ICLR 2017