TY - GEN
T1 - Genetic algorithm based deep learning model selection for visual data classification
AU - Tian, Haiman
AU - Chen, Shu Ching
AU - Shyu, Mei Ling
N1 - Funding Information:
ACKNOWLEDGMENT This research is partially supported by NSF CNS-1461926 and the Dissertation Year Fellowship (DYF) at Florida International University (FIU).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Significant progress has been made by researchers in image classification mainly due to the accessibility of large-scale public visual datasets and powerful Convolutional Neural Network(CNN) models. Pre-trained CNN models can be utilized for learning comprehensive features from smaller training datasets, which support the transfer of knowledge from one source domain to different target domains. Currently, there are numerous frameworks to handle image classifications using transfer learning including preparing the preliminary features from the early layers of pre-trained CNN models, utilizing the mid-/high-level features, and fine-tuning the pre-trained CNN models to work for different targeting domains. In this work, we proposed to build a genetic algorithm-based deep learning model selection framework to address various detection challenges. This framework automates the process of identifying the most relevant and useful features generated by pre-trained models for different tasks. Each model differs in numerous ways depending on the number of layers.
AB - Significant progress has been made by researchers in image classification mainly due to the accessibility of large-scale public visual datasets and powerful Convolutional Neural Network(CNN) models. Pre-trained CNN models can be utilized for learning comprehensive features from smaller training datasets, which support the transfer of knowledge from one source domain to different target domains. Currently, there are numerous frameworks to handle image classifications using transfer learning including preparing the preliminary features from the early layers of pre-trained CNN models, utilizing the mid-/high-level features, and fine-tuning the pre-trained CNN models to work for different targeting domains. In this work, we proposed to build a genetic algorithm-based deep learning model selection framework to address various detection challenges. This framework automates the process of identifying the most relevant and useful features generated by pre-trained models for different tasks. Each model differs in numerous ways depending on the number of layers.
KW - Deep learning model selection
KW - Genetic algorithms
KW - Image classification
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85073216615&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073216615&partnerID=8YFLogxK
U2 - 10.1109/IRI.2019.00032
DO - 10.1109/IRI.2019.00032
M3 - Conference contribution
AN - SCOPUS:85073216615
T3 - Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019
SP - 127
EP - 134
BT - Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019
Y2 - 30 July 2019 through 1 August 2019
ER -