Ensemble classification from deep predictions with test data augmentation
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http://hdl.handle.net/10045/101319
Título: | Ensemble classification from deep predictions with test data augmentation |
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Autor/es: | Calvo-Zaragoza, Jorge | Rico-Juan, Juan Ramón | Gallego, Antonio-Javier |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Convolutional neural networks | Data augmentation | Ensemble classification |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | ene-2020 |
Editor: | Springer Berlin Heidelberg |
Cita bibliográfica: | Soft Computing. 2020, 24(2): 1423-1433. doi:10.1007/s00500-019-03976-7 |
Resumen: | Data augmentation has become a standard step to improve the predictive power and robustness of convolutional neural networks by means of the synthetic generation of new samples depicting different deformations. This step has been traditionally considered to improve the network at the training stage. In this work, however, we study the use of data augmentation at classification time. That is, the test sample is augmented, following the same procedure considered for training, and the decision is taken with an ensemble prediction over all these samples. We present comprehensive experimentation with several datasets and ensemble decisions, considering a rather generic data augmentation procedure. Our results show that performing this step is able to boost the original classification, even when the room for improvement is limited. |
Patrocinador/es: | First author thanks the support from the Spanish Ministerio de Ciencia, Innovación y Universidades through Juan de la Cierva-Formación Grant (Ref. FJCI-2016-27873). |
URI: | http://hdl.handle.net/10045/101319 |
ISSN: | 1432-7643 (Print) | 1433-7479 (Online) |
DOI: | 10.1007/s00500-019-03976-7 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s00500-019-03976-7 |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas |
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2020_Calvo-Zaragoza_etal_SoftComput_final.pdf | Versión final (acceso restringido) | 555,32 kB | Adobe PDF | Abrir Solicitar una copia |
2020_Calvo-Zaragoza_etal_SoftComput_preprint.pdf | Preprint (acceso abierto) | 786,66 kB | Adobe PDF | Abrir Vista previa |
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