Method for classifying images in databases through deep convolutional networks
Since 2006, deep structured learning, or more commonly called deep learning or hierarchical learning, has become a new area of research in machine learning. In recent years, techniques developed from deep learning research have impacted on a wide range of information and particularly image processin...
- Autores:
-
Varela, Noel
Comas-González, Zoe
Ternera-Muñoz, Yesith R
Esmeral-Romero, Ernesto F
Lizardo Zelaya, Nelson Alberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7660
- Acceso en línea:
- https://hdl.handle.net/11323/7660
https://doi.org/10.1016/j.procs.2020.07.022
https://repositorio.cuc.edu.co/
- Palabra clave:
- Dynamic training
Deep convolutional networks
Image classification
- Rights
- openAccess
- License
- CC0 1.0 Universal
| Summary: | Since 2006, deep structured learning, or more commonly called deep learning or hierarchical learning, has become a new area of research in machine learning. In recent years, techniques developed from deep learning research have impacted on a wide range of information and particularly image processing studies, within traditional and new fields, including key aspects of machine learning and artificial intelligence. This paper proposes an alternative scheme for training data management in CNNs, consisting of selective-adaptive data sampling. By means of experiments with the CIFAR10 database for image classification. |
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