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

Alexander Marinov; Nhamo Mtetwa; Hadi Larijani

doi:10.1145/3322134.3322147

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

Alexander Marinov, Nhamo Mtetwa, Hadi Larijani

Cyber Security and Networks

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	ICBDE'19
Subtitle of host publication	Proceedings of the 2019 International Conference on Big Data and Education
Pages	77-81
Number of pages	5
DOIs	https://doi.org/10.1145/3322134.3322147
Publication status	Published - 30 Mar 2019

Keywords

feature cnn
machine learning
scaling
mnist
resnet
deep learning

Documents and Links

10.1145/3322134.3322147

Marinov, A. et al (2019) Application of a Scaled MNIST Dataset Blended with Natural Scene Background on ResNet
© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Marinov, A, Mtetwa, N & Larijani, H 2019, 'Application of a Scaled MNIST Dataset Blended with Natural Scene Background on ResNet'. in ICBDE 2019. International Conference on Big Data and Education, London, United Kingdom, 30-1 April. http://doi.acm.org/10.1145/10.1145/3322134.3322147
Accepted author manuscript, 1.5 MB

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Cite this

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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.",

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