Automated Evolutionary Design of CNN Classifiers for Object Recognition on Satellite Images
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- @Article{POLONSKAIA:2021:PCS,
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author = "Iana S. Polonskaia and Ilya R. Aliev and
Nikolay O. Nikitin",
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title = "Automated Evolutionary Design of {CNN} Classifiers for
Object Recognition on Satellite Images",
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journal = "Procedia Computer Science",
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volume = "193",
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pages = "210--219",
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year = "2021",
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note = "10th International Young Scientists Conference in
Computational Science, YSC2021, 28 June - 2 July,
2021",
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ISSN = "1877-0509",
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DOI = "doi:10.1016/j.procs.2021.10.021",
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URL = "https://www.sciencedirect.com/science/article/pii/S1877050921020627",
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keywords = "genetic algorithms, genetic programming, evolutionary
learning, NAS, CNN, machine learning, recognition,
satellite images",
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abstract = "In the paper, the automated evolutionary approach
FEDOT-NAS for the design of convolutional neural
networks is proposed. It allows building object
recognition models for remote sensing tasks. The
comparison of the proposed approach with
state-of-the-art tools for neural architecture search
is conducted for several examples of satellite-related
datasets. The results of the experiments confirm the
correctness and effectiveness of the proposed approach.
The implementation of FEDOT-NAS is available as an
open-source tool",
- }
Genetic Programming entries for
Iana S Polonskaia
Ilya R Aliev
Nikolay O Nikitin
Citations