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: Proceedings of the 2019 International Conference on Big Data and Education
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages77-81
Number of pages5
ISBN (Print)9781450361866
DOIs
Publication statusPublished - 30 Mar 2019

Keywords

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

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