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Authors: Gabriel Tenorio ; Cristian Villalobos ; Leonardo Mendoza ; Eduardo Costa da Silva and Wouter Caarls

Affiliation: Electrical Engineering Department (DEE), Catholic University of Rio de Janeiro - PUC-Rio, Rio de Janeiro and Brazil

ISBN: 978-989-758-350-6

Keyword(s): Deep Learning, Convolutional Neural Networks, Transfer Learning, Fine Tuning, Data Augmentation, Distributed Learning, Cross Validation, Remote Sensing, Vegetation Monitoring.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: The present paper aims to train and analyze Convolutional Neural Networks (CNN or ConvNets) capable of classifying plant species of a certain region for applications in an environmental monitoring system. In order to achieve this for a limited training dataset, the samples were expanded with the use of a data generator algorithm. Next, transfer learning and fine tuning methods were applied with pre-trained networks. With the purpose of choosing the best layers to be transferred, a statistical dispersion method was proposed. Through a distributed training method, the training speed and performance for the CNN in CPUs was improved. After tuning the parameters of interest in the resulting network by the cross-validation method, the learning capacity of the network was verified. The obtained results indicate an accuracy of about 97%, which was acquired transferring the pre-trained first seven convolutional layers of the VGG-16 network to a new sixteen-layer convolutional network in which the final training was performed. This represents an improvement over the state of the art, which had an accuracy of 91% on the same dataset. (More)

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Paper citation in several formats:
Tenorio, G.; Villalobos, C.; Mendoza, L.; Costa da Silva, E. and Caarls, W. (2019). Improving Transfer Learning Performance: An Application in the Classification of Remote Sensing Data.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 174-183. DOI: 10.5220/0007372201740183

@conference{icaart19,
author={Gabriel Lins Tenorio. and Cristian E. Munoz Villalobos. and Leonardo A. Forero Mendoza. and Eduardo Costa da Silva. and Wouter Caarls.},
title={Improving Transfer Learning Performance: An Application in the Classification of Remote Sensing Data},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={174-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007372201740183},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Improving Transfer Learning Performance: An Application in the Classification of Remote Sensing Data
SN - 978-989-758-350-6
AU - Tenorio, G.
AU - Villalobos, C.
AU - Mendoza, L.
AU - Costa da Silva, E.
AU - Caarls, W.
PY - 2019
SP - 174
EP - 183
DO - 10.5220/0007372201740183

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