Application of a Scaled MNIST Dataset Blended with Natural Scene Background on ResNet

Alexander Marinov, Nhamo Mtetwa, Hadi Larijani

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

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Abstract

Deep learning (DL) has gained a lot of popularity in the science and business community. It has been successful in a range of applications, especially in computer vision. This paper presents results from applying scaled MNIST images dataset to a popular implementation of deep learning called ResNet. This is a valuable contribution because in general convolutional networks are not scale invariant. Our objective is to explore the behavior of a residual neural network when trained and evaluated using three different datasets of scaled MNIST images.
Original languageEnglish
Title of host publicationICBDE'19
Subtitle of host publicationProceedings of the 2019 International Conference on Big Data and Education
Pages77-81
Number of pages5
DOIs
Publication statusPublished - 30 Mar 2019

Keywords

  • feature cnn
  • machine learning
  • scaling
  • mnist
  • resnet
  • deep learning

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