Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Rahimi, Shahram

Committee Member

Archibald, Christopher

Committee Member

Iannucci, Stefano

Date of Degree

12-1-2019

Document Type

Graduate Thesis - Open Access

Major

Computer Science

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

Neural networks (NNs) excel at solving several complex, non-linear problems in the area of supervised learning. A prominent application of these networks is image classification. Numerous improvements over the last few decades have improved the capability of these image classifiers. However, neural networks are still a black-box for solving image classification and other sophisticated tasks. A number of experiments conducted look into exactly how neural networks solve these complex problems. This paper dismantles the neural network solution, incorporating convolution layers, of a specific material classifier. Several techniques are utilized to investigate the solution to this problem. These techniques look at specifically which pixels contribute to the decision made by the NN as well as a look at each neuron’s contribution to the decision. The purpose of this investigation is to understand the decision-making process of the NN and to use this knowledge to suggest improvements to the material classification algorithm.

URI

https://hdl.handle.net/11668/16482

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