masterthesis-latex/presentation.tex

122 lines
4.6 KiB
TeX

\documentclass{beamer}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
%\usepackage{paralist}
%\useoutertheme{infolines}
\usepackage{graphicx}
\usepackage{hyperref}
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citestyle=ieee,
backref=true,
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]{biblatex}
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}
\hypersetup{
pdfauthor=Jim Martens,
pdfstartview=Fit
}
\expandafter\def\expandafter\insertshorttitle\expandafter{%
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\begin{document}
\author{Jim Martens}
\title{Object Detection with Novelty Detection}
\date{\today}
\begin{frame}
\titlepage
\end{frame}
\begin{frame}{Task}
\begin{itemize}
\item neural network trained with one group of classes
\item classification for unknown classes is always wrong
\item how to identify such cases in large set of data?
\end{itemize}
\end{frame}
\begin{frame}{Approaches}
\begin{itemize}
\item auto encoder (Pidhorskyi et al\cite{Pidhorskyi2018}): high error indicates images with unknown classes
\item measure entropy of classification (Miller et al\cite{Miller2018}): exclude high entropy cases
\end{itemize}
\end{frame}
\begin{frame}{Own work and problems}
\begin{itemize}
\item compare performance of auto encoder approach with Miller's approach
\item auto encoder: learns general identity function, therefore error meaningless
\item vanilla SSD: learning issues
\item Bayesian SSD: did not try yet
\end{itemize}
\end{frame}
\begin{frame}{Evaluation}
\begin{itemize}
\item precision, recall, f1 scores, mean average precision
\item open set error
\end{itemize}
\end{frame}
\begin{frame}{References}
\printbibliography
\end{frame}
\end{document}