Two novel Performance improvements for evolving CNN topologies
Two novel Performance improvements for evolving CNN topologies
Convolutional Neural Networks (CNNs) are the state-of-the-art algorithms for the processing of images. However the configuration and training of these networks is a complex task requiring deep domain knowledge, experience and much trial and error. Using genetic algorithms, competitive CNN topologies for image recognition can be produced for any specific purpose, however in previous work this has come at high computational cost. In this work two novel approaches are presented to the utilisation of these algorithms, effective in reducing complexity and training time by nearly 20%. This is accomplished via regularisation directly on training time, and the use of partial training to enable early ranking of individual architectures. Both approaches are validated on the benchmark CIFAR10 data set, and maintain accuracy.
Computer Vision, Genetic Algorithm
Strauch, Yaron Leander
a246b519-5a8a-4011-b330-7184abc055eb
Grundy, Jo
0bc72187-8dce-41fc-b809-93a6adbe0980
10 February 2021
Strauch, Yaron Leander
a246b519-5a8a-4011-b330-7184abc055eb
Grundy, Jo
0bc72187-8dce-41fc-b809-93a6adbe0980
Strauch, Yaron Leander and Grundy, Jo
(2021)
Two novel Performance improvements for evolving CNN topologies.
Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Online, Vancouver, Canada.
02 - 09 Feb 2021.
5 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Convolutional Neural Networks (CNNs) are the state-of-the-art algorithms for the processing of images. However the configuration and training of these networks is a complex task requiring deep domain knowledge, experience and much trial and error. Using genetic algorithms, competitive CNN topologies for image recognition can be produced for any specific purpose, however in previous work this has come at high computational cost. In this work two novel approaches are presented to the utilisation of these algorithms, effective in reducing complexity and training time by nearly 20%. This is accomplished via regularisation directly on training time, and the use of partial training to enable early ranking of individual architectures. Both approaches are validated on the benchmark CIFAR10 data set, and maintain accuracy.
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Published date: 10 February 2021
Venue - Dates:
Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Online, Vancouver, Canada, 2021-02-02 - 2021-02-09
Keywords:
Computer Vision, Genetic Algorithm
Identifiers
Local EPrints ID: 446487
URI: http://eprints.soton.ac.uk/id/eprint/446487
PURE UUID: 773bc97a-99d0-4d3e-abf0-63f4c4cc5cf7
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Date deposited: 11 Feb 2021 17:33
Last modified: 17 Mar 2024 03:55
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Contributors
Author:
Yaron Leander Strauch
Author:
Jo Grundy
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