Multi-objective Evolution for Deep Neural Network Architecture Search
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00534837" target="_blank" >RIV/67985807:_____/20:00534837 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-63836-8_23" target="_blank" >http://dx.doi.org/10.1007/978-3-030-63836-8_23</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-63836-8_23" target="_blank" >10.1007/978-3-030-63836-8_23</a>
Alternative languages
Result language
angličtina
Original language name
Multi-objective Evolution for Deep Neural Network Architecture Search
Original language description
In this paper, we propose a multi-objective evolutionary algorithm for automatic deep neural architecture search. The algorithm optimizes the performance of the model together with the number of network parameters. This allows exploring architectures that are both successful and compact. We test the proposed solution on several image classification data sets including MNIST, fashionMNIST and CIFAR-10, and we consider deep architectures including convolutional and fully connected networks. The effects of using two different versions of multi-objective selections are also examined in the paper. Our approach outperforms both the considered baseline architectures and the standard genetic algorithm used in our previous work.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-23827S" target="_blank" >GA18-23827S: Capabilities and limitations of shallow and deep networks</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Neural Information Processing. ICONIP 2020 Proceedings, Part III
ISBN
978-3-030-63835-1
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
270-281
Publisher name
Springer
Place of publication
Cham
Event location
Bangkok
Event date
Nov 23, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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