Genetic algorithm based deep learning model selection for visual data classification

Haiman Tian, Shu Ching Chen, Mei Ling Shyu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages127-134
Number of pages8
ISBN (Electronic)9781728113371
DOIs
StatePublished - Jul 2019
Event20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019 - Los Angeles, United States
Duration: Jul 30 2019Aug 1 2019

Publication series

NameProceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019

Conference

Conference20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019
CountryUnited States
CityLos Angeles
Period7/30/198/1/19

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Keywords

  • Deep learning model selection
  • Genetic algorithms
  • Image classification
  • Transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems

Cite this

Tian, H., Chen, S. C., & Shyu, M. L. (2019). Genetic algorithm based deep learning model selection for visual data classification. In Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019 (pp. 127-134). [8843497] (Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRI.2019.00032