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International Conference On Secure Knowledge Management In Artificial Intelligence Era

SKM 2021: Secure Knowledge Management In The Artificial Intelligence Era pp 130–148Cite as

Analyzing CNN Models’ Sensitivity to the Ordering of Non-natural Data

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1549))

Abstract

Convolutional Neural Networks (CNN) have revolutionized image recognition technology, and has found uses in various non-image related fields. When dealing with non-natural data, where the ordering of various parts of a data sample is not dictated by nature, it is known that a model trained on certain orderings of the data performs better than models trained on other orderings. Understanding how to best order the training data for improving CNN performance is not well-studied. In this paper, we investigate this problem by examining several different CNN models. We define a functional algorithm for ordering, show that order importance in CNNs is model dependent and that depending on the model, statistical relationships are an important tool in establishing order with better performance.

This work is partially supported by NSF grants HRD-1736209 and CNS-1553696.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Randy Klepetko & Ram Krishnan

Authors
  1. Randy Klepetko
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  2. Ram Krishnan
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Corresponding author

Correspondence to Randy Klepetko .

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Editors and Affiliations

  1. The University of Texas at San Antonio, San Antonio, TX, USA

    Ram Krishnan

  2. The University of Texas at San Antonio, San Antonio, TX, USA

    H. Raghav Rao

  3. Birla Institute of Technology and Science, Pilani, Rajasthan, India

    Sanjay K. Sahay

  4. Indiana University, Bloomington, IN, USA

    Sagar Samtani

  5. SUNY Buffalo, Buffalo, NY, USA

    Ziming Zhao

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Klepetko, R., Krishnan, R. (2022). Analyzing CNN Models’ Sensitivity to the Ordering of Non-natural Data. In: Krishnan, R., Rao, H.R., Sahay, S.K., Samtani, S., Zhao, Z. (eds) Secure Knowledge Management In The Artificial Intelligence Era. SKM 2021. Communications in Computer and Information Science, vol 1549. Springer, Cham. https://doi.org/10.1007/978-3-030-97532-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-97532-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97531-9

  • Online ISBN: 978-3-030-97532-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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