CollabNet - Collaborative Deep Learning Network

Moisés Lima Junior, Will Almeida, Areolino Neto

2019

Abstract

The goal is an improvement on learning of deep neural networks. This improvement is here called the CollabNet network, which consists of a new method of insertion of new layers hidden in deep feedforward neural networks, changing the traditional way of stacking autoencoders. The new form of insertion is considered collaborative and seeks to improve the training against approaches based on stacked autoencoders. In this new approach, the addition of a new layer is carried out in a coordinated and gradual way, keeping under the control of the designer the influence of this new layer in training and no longer in a random and stochastic way as in the traditional stacking. The collaboration proposed in this work consists of making the learning of newly inserted layer continuing the learning obtained from previous layers, without prejudice to the global learning of the network. In this way, the freshly added layer collaborates with the previous layers and the set works in a way more aligned to the learning. CollabNet has been tested in the Wisconsin Breast Cancer Dataset database, obtaining a satisfactory and promising result.

Paper Citation

in Harvard Style

Lima Junior M., Almeida W. and Neto A. (2019). CollabNet - Collaborative Deep Learning Network.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 685-692. DOI: 10.5220/0007395106850692

in Bibtex Style

@conference{icaart19,
author={Moisés Lima Junior and Will Almeida and Areolino Neto},
title={CollabNet - Collaborative Deep Learning Network},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={685-692},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007395106850692},
isbn={978-989-758-350-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - CollabNet - Collaborative Deep Learning Network
SN - 978-989-758-350-6
AU - Lima Junior M.
AU - Almeida W.
AU - Neto A.
PY - 2019
SP - 685
EP - 692
DO - 10.5220/0007395106850692