Auburn Research Symposium Template
Author
Michael A. Alcorn
Last Updated
6年前
License
Creative Commons CC BY 4.0
Abstract
The official abstract template for the Auburn University Research Symposium.
The official abstract template for the Auburn University Research Symposium.
\documentclass[12pt]{article}
\usepackage{amsmath,amsfonts,amssymb}
\usepackage{gensymb}
\usepackage[left=2cm, right=2cm, top=2cm]{geometry}
\usepackage{hyperref}
\usepackage[utf8]{inputenc}
\usepackage{mathptmx}
\hypersetup{
colorlinks=true,
urlcolor=blue
}
\setlength\parindent{0pt}
\begin{document}
\begin{center}
Auburn Research: Student Symposium \\ Tuesday, April 9\textsuperscript{th}, Auburn University Student Center
\end{center}
Please use this \LaTeX{} template for your abstract. The Auburn University Library provides a wealth of resources on how to edit and format \LaTeX{} documents at: \texttt{\href{https://libguides.auburn.edu/LaTeX}{https://libguides.auburn.edu/LaTeX}}.
\begin{center}
\textbf{INSTRUCTIONS}
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\begin{enumerate}
\item Edit this template in a \LaTeX{} editor, like \href{https://www.overleaf.com}{Overleaf}. You should \textbf{only} change fields that have \texttt{\%~CHANGE THIS} above them. Do \textbf{NOT} delete any of the field names, e.g., \texttt{\textbackslash textbf\{Title:\}}. Descriptions \textbf{cannot} be more than \textbf{2,000 characters}, \emph{including spaces} (this will be automatically checked by a computer!). The title, author information, and affiliation are not part of the character count. Do not include figures or references in your abstract.
\item Proofread your abstract---it will appear as submitted!
\item Save a copy of this abstract template to your computer and label the file as \texttt{YOURLASTNAME.tex}.
\item Upload the file with your Student Symposium 2019 registration (instructions on the registration form).
\item Abstracts are due February 8, 2019, by 11:59 PM CST.
\end{enumerate}
\bigskip
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% Use sentence case in the title
\textbf{Title:} Strike (with) a pose: neural networks are easily fooled by strange poses of familiar objects
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% Author name should be ordered: Last name, First name, Middle initial
\textbf{Primary Author (and presenter):} Alcorn, Michael, A.
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% Author names should be ordered: 2nd Author Last name, First name; 3rd Author Last name, First name; and so on
\textbf{Additional Authors:} Li, Qi; Gong, Zhitao; Wang, Chengfei; Mai, Long; Ku, Wei-Shinn; Nguyen, Anh;
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% Use Title Case for the Department Name
\textbf{Department:} Department of Computer Science and Software Engineering
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% Use Title Case for the School/College Name
\textbf{College/School:} Samuel Ginn College of Engineering
\bigskip
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\textbf{Description:} Deep neural networks (DNNs) are increasingly common components of computer vision systems. When handling ``familiar'' data, DNNs are capable of superhuman performance; however, inputs that are dissimilar to previously encountered examples (but that are still easily recognized by humans) can cause DNNs to make catastrophic mistakes. Here, we present a framework for discovering DNN failures that harnesses 3D computer graphics. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to ``strange'' poses of well-known objects. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97\% of their pose space. Further, DNNs are highly sensitive to slight pose perturbations; for example, rotating a correctly classified object as little as $8\degree$ can often cause a DNN to misclassify. Lastly, 75\% to 99\% of adversarial poses transfer to DNNs with different architectures and/or trained with different datasets.
\end{document}