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Appropriateness of Numbers of Receptive Fields in Convolutional Neural Networks Based on Classifying CIFAR-10 and EEACL26 Datasets Cover

Appropriateness of Numbers of Receptive Fields in Convolutional Neural Networks Based on Classifying CIFAR-10 and EEACL26 Datasets

Electrical, Control and Communication Engineering
Volume 14 (2018): Issue 2 (December 2018)
By:   
Open Access
|Mar 2019
Authors

Authors

Vadim Romanuke

romanukevadimv@gmail.com

Polish Naval Academy, Gdynia, Poland
DOI: https://doi.org/10.2478/ecce-2018-0019 | Journal eISSN: 2255-9159
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Language: English
Page range: 157 - 163
Published on: Mar 12, 2019
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
Convolutional neural networks,
Convolutional layers,
Filters,
Performance,
Receptive fields
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2019 Vadim Romanuke, published by Riga Technical University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 14 (2018): Issue 2 (December 2018)
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