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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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)
Number of pages5
ISBN (Electronic)9781450361866
ISBN (Print)9781450361866
Publication statusPublished - 30 Mar 2019

Publication series

NameACM International Conference Proceeding Series


  • feature cnn
  • machine learning
  • scaling
  • mnist
  • resnet
  • deep learning
  • Deep learning
  • Feature cnn
  • Machine learning
  • Mnist
  • Scaling
  • Resnet

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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