Degree Type

Creative Component

Semester of Graduation

Summer 2019

Department

Electrical and Computer Engineering

First Major Professor

Joseph Zambreno

Degree(s)

Master of Science (MS)

Major(s)

Computer Engineering

Abstract

Since the ImageNet Large Scale Visual Recognition Challenge has been run annually from 2010 to present, researchers have designed lots of brilliant deep convolutional neural networks(D-CNNs). However, most of the existing deep convolutional neural networks are trained with large datasets. It is rare for small datasets to take advantage of deep convolutional neural networks because of overfitting when implementing those models. In this report, I propose a modified deep neural network and use this model to fit a small size dataset. The goal of my work is to show that a proper modified very deep model pre-trained on ImageNet for image classification can be used to fit very small dataset without severe overfitting.

Copyright Owner

Shu, Mengying

File Format

PDF

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