Reuse of a Deep Learning model for handwritten digit recognition

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Machine Learning (ML) techniques have made significant advances in solving various problems, which has led to wide dissemination in their use and development. Currently there are different models that have achieved a high level of performance, which raises the question of what to do when we face a p...

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Detalles Bibliográficos
Autores principales: Pacchiotti, Mauro José, Ballejos, Luciana C., Ale, Mariel Alejandra
Formato: Articulo
Lenguaje:Español
Publicado: 2024
Materias:
Ciencias Informáticas
Transfer Learning
model reuse
Machine Learning
handwritten digit recognition
Transferencia de Aprendizaje
Reúso de modelos
Aprendizaje Automático
Reconocimiento de dígitos numéricos
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/168752
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-168752
record_format dspace
spelling I19-R120-10915-1687522024-08-20T20:04:04Z http://sedici.unlp.edu.ar/handle/10915/168752 Reuse of a Deep Learning model for handwritten digit recognition Reúso de un modelo de Aprendizaje Profundo para reconocimiento de dígitos manuscritos Pacchiotti, Mauro José Ballejos, Luciana C. Ale, Mariel Alejandra 2024-04 2024-08-20T17:53:25Z es Ciencias Informáticas Transfer Learning model reuse Machine Learning handwritten digit recognition Transferencia de Aprendizaje Reúso de modelos Aprendizaje Automático Reconocimiento de dígitos numéricos Machine Learning (ML) techniques have made significant advances in solving various problems, which has led to wide dissemination in their use and development. Currently there are different models that have achieved a high level of performance, which raises the question of what to do when we face a problem for which a very efficient model already exists. This scenario has, for some time, promoted the research and development of different techniques to reuse these models, instead of undertaking the design, implementation, and training of a new one, with all the effort that this entails. In this work, a classification problem is presented, and the reuse of a convolutional neural network is proposed with the objective of recognizing handwritten numbers. Likewise, the performance of the reused model has been evaluated. Las técnicas de Aprendizaje Automático (AA) han avanzado significativamente en la solución de diversos problemas, lo que ha llevado a una amplia difusión en su uso y desarrollo. Actualmente existen distintos modelos que han alcanzado un alto nivel de desempeño, lo que plantea la duda de qué hacer cuando nos enfrentamos a un problema para el cual ya existe un modelo muy eficiente. Desde hace tiempo esta situación ha impulsado la investigación y el desarrollo de diferentes técnicas para reutilizar estos modelos, en lugar de emprender el diseño, implementación y entrenamiento de uno nuevo, con todo el esfuerzo que ello conlleva. En este trabajo se presenta un problema de clasificación y se propone la reutilización de una red neuronal convolucional con el objetivo de reconocer números manuscritos. Asimismo, se ha evaluado el desempeño del modelo reutilizado. Sociedad Argentina de Informática e Investigación Operativa Articulo Articulo http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) application/pdf 43-57
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
Transfer Learning
model reuse
Machine Learning
handwritten digit recognition
Transferencia de Aprendizaje
Reúso de modelos
Aprendizaje Automático
Reconocimiento de dígitos numéricos
spellingShingle Ciencias Informáticas
Transfer Learning
model reuse
Machine Learning
handwritten digit recognition
Transferencia de Aprendizaje
Reúso de modelos
Aprendizaje Automático
Reconocimiento de dígitos numéricos
Pacchiotti, Mauro José
Ballejos, Luciana C.
Ale, Mariel Alejandra
Reuse of a Deep Learning model for handwritten digit recognition
topic_facet Ciencias Informáticas
Transfer Learning
model reuse
Machine Learning
handwritten digit recognition
Transferencia de Aprendizaje
Reúso de modelos
Aprendizaje Automático
Reconocimiento de dígitos numéricos
description Machine Learning (ML) techniques have made significant advances in solving various problems, which has led to wide dissemination in their use and development. Currently there are different models that have achieved a high level of performance, which raises the question of what to do when we face a problem for which a very efficient model already exists. This scenario has, for some time, promoted the research and development of different techniques to reuse these models, instead of undertaking the design, implementation, and training of a new one, with all the effort that this entails. In this work, a classification problem is presented, and the reuse of a convolutional neural network is proposed with the objective of recognizing handwritten numbers. Likewise, the performance of the reused model has been evaluated.
format Articulo
Articulo
author Pacchiotti, Mauro José
Ballejos, Luciana C.
Ale, Mariel Alejandra
author_facet Pacchiotti, Mauro José
Ballejos, Luciana C.
Ale, Mariel Alejandra
author_sort Pacchiotti, Mauro José
title Reuse of a Deep Learning model for handwritten digit recognition
title_short Reuse of a Deep Learning model for handwritten digit recognition
title_full Reuse of a Deep Learning model for handwritten digit recognition
title_fullStr Reuse of a Deep Learning model for handwritten digit recognition
title_full_unstemmed Reuse of a Deep Learning model for handwritten digit recognition
title_sort reuse of a deep learning model for handwritten digit recognition
publishDate 2024
url http://sedici.unlp.edu.ar/handle/10915/168752
work_keys_str_mv AT pacchiottimaurojose reuseofadeeplearningmodelforhandwrittendigitrecognition
AT ballejoslucianac reuseofadeeplearningmodelforhandwrittendigitrecognition
AT alemarielalejandra reuseofadeeplearningmodelforhandwrittendigitrecognition
AT pacchiottimaurojose reusodeunmodelodeaprendizajeprofundoparareconocimientodedigitosmanuscritos
AT ballejoslucianac reusodeunmodelodeaprendizajeprofundoparareconocimientodedigitosmanuscritos
AT alemarielalejandra reusodeunmodelodeaprendizajeprofundoparareconocimientodedigitosmanuscritos
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