Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Fran Jurišić ; Ivan Filković and Zoran Kalafatić

Affiliation: University of Zagreb, Croatia

Keyword(s): Convolutional Neural Network, Commitee, Ensemble.

Abstract: Many high performing deep learning models for image classification put their base models in a committee as a final step to gain competitive edge. In this paper we focus on that aspect, analyzing how committee size and makeup of models trained with different preprocessing methods impact final performance. Working with two datasets, representing both rigid and non-rigid object classification in German Traffic Sign Recognition Benchmark (GTSRB) and CIFAR-10, and two preprocessing methods in addition to original images, we report performance improvements and compare them. Our experiments cover committees trained on just one dataset variation as well as hybrid ones, unreliability of small committees of low error models and performance metrics specific to the way committees are built. We point out some guidelines to predict committee behavior and good approaches to analyze their impact and limitations.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.191.229.157

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Jurišić, F., Filković, I. and Kalafatić, Z. (2016). Evaluating the Effects of Convolutional Neural Network Committees. In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP; ISBN 978-989-758-175-5; ISSN 2184-4321, SciTePress, pages 560-565. DOI: 10.5220/0005719305600565

@conference{visapp16,
author={Fran Jurišić and Ivan Filković and Zoran Kalafatić},
title={Evaluating the Effects of Convolutional Neural Network Committees},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP},
year={2016},
pages={560-565},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005719305600565},
isbn={978-989-758-175-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP
TI - Evaluating the Effects of Convolutional Neural Network Committees
SN - 978-989-758-175-5
IS - 2184-4321
AU - Jurišić, F.
AU - Filković, I.
AU - Kalafatić, Z.
PY - 2016
SP - 560
EP - 565
DO - 10.5220/0005719305600565
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic