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BEHAVIORAL ANALYSIS OF DEEP CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION

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Date Issued:
2022
Abstract/Description:
Within Deep CNNs there is great excitement over breakthroughs in network performance on benchmark datasets such as ImageNet. Around the world competitive teams work on new ways to innovate and modify existing networks, or create new ones that can reach higher and higher accuracy levels. We believe that this important research must be supplemented with research into the computational dynamics of the networks themselves. We present research into network behavior as it is affected by: variations in the number of filters per layer, pruning filters during and after training, collapsing the weight space of the trained network using a basic quantization, and the effect of Image Size and Input Layer Stride on training time and test accuracy. We provide insights into how the total number of updatable parameters can affect training time and accuracy, and how “time per epoch” and “number of epochs” affect network training time. We conclude with statistically significant models that allow us to predict training time as a function of total number of updatable parameters in the network.
Title: BEHAVIORAL ANALYSIS OF DEEP CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION.
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Name(s): Clark, James Alex , author
Barenholtz, Elan , Thesis advisor
Florida Atlantic University, Degree grantor
Center for Complex Systems and Brain Sciences
Charles E. Schmidt College of Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2022
Date Issued: 2022
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 151 p.
Language(s): English
Abstract/Description: Within Deep CNNs there is great excitement over breakthroughs in network performance on benchmark datasets such as ImageNet. Around the world competitive teams work on new ways to innovate and modify existing networks, or create new ones that can reach higher and higher accuracy levels. We believe that this important research must be supplemented with research into the computational dynamics of the networks themselves. We present research into network behavior as it is affected by: variations in the number of filters per layer, pruning filters during and after training, collapsing the weight space of the trained network using a basic quantization, and the effect of Image Size and Input Layer Stride on training time and test accuracy. We provide insights into how the total number of updatable parameters can affect training time and accuracy, and how “time per epoch” and “number of epochs” affect network training time. We conclude with statistically significant models that allow us to predict training time as a function of total number of updatable parameters in the network.
Identifier: FA00013940 (IID)
Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2022.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Neural networks (Computer science)
Image processing
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013940
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.