Evolutionary Programming Based Deep Feature and Model Selection for Visual Data Classification

Haiman Tian, Shu Ching Chen, Mei Ling Shyu

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

Abstract

Deep Learning (DL) has made significant changes to a large number of research areas in recent decades. For example, several astonishing Convolutional Neural Network (CNN) models have been built by researchers to fulfill image classification needs using large-scale visual datasets successfully. Transfer Learning (TL) makes use of those pre-Trained models to ease the feature learning process for other target domains that contain a smaller amount of training data. Currently, there are numerous ways to utilize features generated by transfer learning. Pre-Trained CNN models prepare mid-/high-level features to work for different targeting problem domains. In this paper, a DL feature and model selection framework based on evolutionary programming is proposed to solve the challenges in visual data classification. It automates the process of discovering and obtaining the most representative features generated by the pre-Trained DL models for different classification tasks.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages61-66
Number of pages6
ISBN (Electronic)9781728142722
DOIs
StatePublished - Aug 2020
Event3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020 - Shenzhen, Guangdong, China
Duration: Aug 6 2020Aug 8 2020

Publication series

NameProceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020

Conference

Conference3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
CountryChina
CityShenzhen, Guangdong
Period8/6/208/8/20

Keywords

  • deep learning
  • evolutionary programming
  • image classification
  • transfer learning

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Media Technology
  • Modeling and Simulation
  • Library and Information Sciences

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