Colloquium presentation added
Signed-off-by: Jim Martens <github@2martens.de>
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\documentclass{beamer}
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\usepackage[T1]{fontenc}
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\usepackage[utf8]{inputenc}
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\usepackage[english]{babel}
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%\usepackage{paralist}
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%\useoutertheme{infolines}
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\usepackage{graphicx}
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\usepackage{hyperref}
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\usepackage{listings}
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\usepackage{color}
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\usepackage{textcomp}
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\usepackage{csquotes}
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\usetheme{Warsaw}
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\usecolortheme{crane}
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\pagenumbering{arabic}
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\def\thesection{\arabic{section})}
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\def\thesubsection{\alph{subsection})}
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\def\thesubsubsection{(\roman{subsubsection})}
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\setbeamertemplate{navigation symbols}{}
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\graphicspath{ {src/} {/home/jim/Pictures/} }
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\definecolor{mymauve}{rgb}{0.58,0,0.82}
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\usepackage[
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backend=biber,
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bibstyle=authoryear,
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citestyle=authoryear,
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minnames=1,
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maxnames=2
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]{biblatex}
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\addbibresource{ma.bib}
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\MakeOuterQuote{"}
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\lstset{ %
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backgroundcolor=\color{white}, % choose the background color; you must add \usepackage{color} or \usepackage{xcolor}
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basicstyle=\footnotesize, % the size of the fonts that are used for the code
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breakatwhitespace=false, % sets if automatic breaks should only happen at whitespace
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breaklines=true, % sets automatic line breaking
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captionpos=b, % sets the caption-position to bottom
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commentstyle=\color{mygray}, % comment style
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deletekeywords={}, % if you want to delete keywords from the given language
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escapeinside={\%*}{*)}, % if you want to add LaTeX within your code
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extendedchars=true, % lets you use non-ASCII characters; for 8-bits encodings only, does not work with UTF-8
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keepspaces=true, % keeps spaces in text, useful for keeping indentation of code (possibly needs columns=flexible)
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keywordstyle=\color{blue}, % keyword style
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language=PHP, % the language of the code
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morekeywords={class, function, return, protected, public, private, const, static, new, extends, namespace, null}, % if you want to add more keywords to the set
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numbers=left, % where to put the line-numbers; possible values are (none, left, right)
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numbersep=5pt, % how far the line-numbers are from the code
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numberstyle=\tiny\color{mygray}, % the style that is used for the line-numbers
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rulecolor=\color{black}, % if not set, the frame-color may be changed on line-breaks within not-black text (e.g. comments (green here))
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showspaces=false, % show spaces everywhere adding particular underscores; it overrides 'showstringspaces'
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showstringspaces=false, % underline spaces within strings only
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showtabs=false, % show tabs within strings adding particular underscores
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stepnumber=2, % the step between two line-numbers. If it's 1, each line will be numbered
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stringstyle=\color{mygreen}, % string literal style
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tabsize=2, % sets default tabsize to 2 spaces
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title=\lstname % show the filename of files included with \lstinputlisting; also try caption instead of title
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}
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\hypersetup{
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pdfauthor=Jim Martens,
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pdfstartview=Fit
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}
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\expandafter\def\expandafter\insertshorttitle\expandafter{%
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\raggedleft \insertframenumber\,/\,\inserttotalframenumber\;}
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\begin{document}
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\author{Jim 2martens}
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\title{Novelty detection for object detection in open set conditions}
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\date{October 18th, 2019}
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\begin{frame}
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\titlepage
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\end{frame}
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\begin{frame}{Contents}
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\tableofcontents
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\end{frame}
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\section{Motivation}
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\begin{frame}{Problem}
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\begin{itemize}
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\item network trained with one set of objects
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\vfill
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\item encounters other objects in reality
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\vfill
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\item cannot properly classify or detect unknown
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object types
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\end{itemize}
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\end{frame}
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\begin{frame}{Open Set}
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\centering
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\begin{figure}[h]
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\includegraphics[scale=0.8]{images/open-set.pdf}
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\caption{Open set problem: test set contains classes that were not present during training. Resembles figure 1 of \cite{Miller2018}}
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\end{figure}
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\end{frame}
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\begin{frame}{Approaches}
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\begin{itemize}
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\item somehow detect or identify novel input
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\vfill
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\item \cite{Pimentel2014} provide overview of different approaches
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\vfill
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\item will look at reconstruction-based novelty detection
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\vfill
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\item in particular, dropout sampling
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\end{itemize}
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\end{frame}
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\begin{frame}{Hypothesis}
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\centering
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Dropout sampling delivers better object detection performance under open set conditions compared to object detection without it.
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\end{frame}
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\section{Background}
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\begin{frame}{Related Works}
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\begin{itemize}
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\item Bayesian networks can be used to model uncertainty (\cite{Ghahramani2015})
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\vfill
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\item \cite{Gal2017} shows that dropout training resembles a general approximate Bayesian model
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\vfill
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\item \cite{Teye2018} argue that most modern networks have adopted other regularisation techniques
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\vfill
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\item \cite{Ioffe2015} introduce batch normalisation
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\end{itemize}
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\end{frame}
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\section{Methods}
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\subsection{Structure}
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\begin{frame}{Vanilla SSD}
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\centering
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\begin{figure}[h]
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\includegraphics{images/vanilla-ssd.pdf}
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\caption{Vanilla SSD network as defined by \cite{Liu2016}}
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\end{figure}
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\begin{itemize}
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\item detects objects of differing sizes
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\end{itemize}
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\end{frame}
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\begin{frame}{Bayesian SSD}
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\centering
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\begin{figure}[h]
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\includegraphics[scale=0.95]{images/bayesian-ssd.pdf}
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\caption{Bayesian SSD network as defined by \cite{Miller2018}}
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\end{figure}
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\begin{itemize}
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\item adds dropout layers and multiple forward passes
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\end{itemize}
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\end{frame}
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\subsection{Decoding Pipelines}
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\begin{frame}{Vanilla SSD}
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\begin{enumerate}
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\item (batch\_size, \#nr\_boxes, \#nr\_classes + 12)
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\vfill
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\item transforming relative into absolute coordinates
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\vfill
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\item per-class confidence threshold
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\vfill
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\item per-class non-maximum suppression
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\vfill
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\item top \(k\)
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\end{enumerate}
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\end{frame}
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\begin{frame}{Vanilla SSD with Entropy Threshold}
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\begin{enumerate}
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\item (batch\_size, \#nr\_boxes, \#nr\_classes + 12)
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\vfill
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\item transforming relative into absolute coordinates
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\vfill
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\item \textbf{entropy thresholding}
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\vfill
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\item per-class confidence threshold
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\vfill
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\item per-class non-maximum suppression
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\vfill
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\item top \(k\)
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\end{enumerate}
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\end{frame}
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\begin{frame}{Bayesian SSD}
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\begin{enumerate}
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\item (batch\_size, \#nr\_boxes \(\cdot\) \#nr\_forward\_passes, \#nr\_classes + 12)
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\vfill
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\item transforming relative into absolute coordinates
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\vfill
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\item confidence threshold for class with highest confidence
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\vfill
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\item discard predictions with background 0.8 or higher
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\vfill
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\item partition into observations
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\vfill
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\item entropy thresholding
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\vfill
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\item per-class confidence threshold
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\vfill
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\item per-class non-maximum suppression
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\vfill
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\item top \(k\)
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\end{enumerate}
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\end{frame}
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\section{Results and Interpretation}
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\subsection{Data}
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\begin{frame}{MS COCO}
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\begin{table}[htbp]
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\centering
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\begin{tabular}{rcc}
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\hline
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class & number of & percentage of \\
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& detections & total \\
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\hline
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total & 31,991 & 100\% \\
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persons & 10,988 & 34.3\% \\
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cars & 1,932 & 6\% \\
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chairs & 1,791 & 5.6\% \\
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bottles & 1,021 & 3.2\% \\
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cups & 898 & 2.8\% \\
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\hline
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\end{tabular}
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\caption{Number of ground truth detections per class (top 5).}
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\end{table}
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\end{frame}
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\subsection{Results}
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\begin{frame}[allowframebreaks]{Micro Averaging}
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\begin{table}[ht]
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\begin{tabular}{rcccc}
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\hline
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& max & abs OSE & Recall & Precision\\
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& \(F_1\) Score & \multicolumn{3}{c}{at max \(F_1\) point} \\
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\hline
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0.01 conf & 0.255 & 3176 & 0.214 & 0.318 \\
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0.2 conf & \textbf{0.376} & 2939 & \textbf{0.382} & 0.372 \\
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entropy - 0.01 conf & 0.255 & 3168 & 0.214 & 0.318 \\
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% entropy} thresh: 2.4 for vanilla} SSD} is best
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\hline
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\end{tabular}
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\caption{Vanilla SSD rounded results for micro averaging.}
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\end{table}
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\begin{table}[ht]
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\begin{tabular}{rcccc}
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\hline
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& max & abs OSE & Recall & Precision\\
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& \(F_1\) Score & \multicolumn{3}{c}{at max \(F_1\) point} \\
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\hline
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no DO - 0.2 conf & 0.209 & 2709 & 0.300 & 0.161 \\
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no DO - 0.2 conf - NMS& 0.371 & \textbf{2335} & 0.365 & \textbf{0.378} \\
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0.9 keep - 0.2 conf - NMS & 0.359 & 2584 & 0.363 & 0.357 \\
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0.5 keep - 0.2 conf - NMS & 0.325 & 2759 & 0.342 & 0.311 \\
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\hline
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\end{tabular}
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\caption{Bayesian SSD rounded results for micro averaging. All variants use 10 forward passes.}
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\end{table}
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\end{frame}
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\begin{frame}[allowframebreaks]{Macro Averaging}
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\begin{table}[ht]
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\begin{tabular}{rcccc}
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\hline
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& max & abs OSE & Recall & Precision\\
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& \(F_1\) Score & \multicolumn{3}{c}{at max \(F_1\) point} \\
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\hline
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0.01 conf & 0.370 & 1426 & 0.328 & 0.424 \\
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0.2 conf & \textbf{0.375} & 1218 & \textbf{0.338} & 0.424 \\
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entropy - 0.01 conf & 0.370 & 1373 & 0.329 & \textbf{0.425} \\
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\hline
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\end{tabular}
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\caption{Vanilla SSD rounded results for macro averaging.}
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\end{table}
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\begin{table}[ht]
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\begin{tabular}{rcccc}
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\hline
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& max & abs OSE & Recall & Precision\\
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& \(F_1\) Score & \multicolumn{3}{c}{at max \(F_1\) point} \\
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\hline
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no DO - 0.2 conf & 0.226 & \textbf{809} & 0.229 & 0.224 \\
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no DO - 0.2 conf - NMS & 0.363 & 1057 & 0.321 & 0.420 \\
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0.9 keep - 0.2 conf - NMS & 0.355 & 1137 & 0.320 & 0.399 \\
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0.5 keep - 0.2 conf - NMS & 0.322 & 1264 & 0.307 & 0.340 \\
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\hline
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\end{tabular}
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\caption{Bayesian SSD rounded results for macro averaging. All variants use 10 forward passes.}
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\end{table}
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\end{frame}
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\begin{frame}{Example: Vanilla SSD}
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\centering
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\begin{figure}[h]
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\includegraphics[width=\textwidth]{images/COCO_val2014_000000336587_bboxes_vanilla.png}
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\end{figure}
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\end{frame}
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\begin{frame}{Example: Bayesian SSD 0.9 keep ratio}
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\centering
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\begin{figure}[h]
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\includegraphics[width=\textwidth]{images/COCO_val2014_000000336587_bboxes_bayesian.png}
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\end{figure}
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\end{frame}
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\subsection{Interpretation}
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\begin{frame}[allowframebreaks]{Effect of NMS}
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\begin{table}[tbp]
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\centering
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\begin{tabular}{rccc}
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\hline
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variant & before & after & after \\
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& entropy/NMS & entropy/NMS & top \(k\) \\
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\hline
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no DO, no NMS & 155,251 & 122,868 & 72,207 \\
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no DO, NMS & 155,250 & 36,061 & 33,827 \\
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\hline
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\end{tabular}
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\caption{Comparison of Bayesian SSD variants without dropout with
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respect to the number of detections before the entropy threshold,
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after it and/or NMS, and after top \(k\). The
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entropy threshold 1.5 was used for both.}
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\end{table}
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\begin{table}[htbp]
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\centering
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\begin{tabular}{rccc}
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\hline
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variant & before & after & after \\
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& entropy/NMS & entropy/NMS & top \(k\) \\
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\hline
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no DO, no NMS & 19,014 & 48,484 & 27,707 \\
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no DO, NMS & 19,014 & 14,542 & 13,486 \\
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\hline
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\end{tabular}
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\caption{Comparison of Bayesian SSD variants without dropout with
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respect to the number of detections before the entropy threshold,
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after it and/or NMS, and after top \(k\). The
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entropy threshold 1.5 was used for both. The numbers are for
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the persons class.}
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\end{table}
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\begin{table}[htbp]
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\centering
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\begin{tabular}{rccc}
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\hline
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variant & before & after & after \\
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& entropy/NMS & entropy/NMS & top \(k\) \\
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\hline
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no DO, no NMS & 1,011 & 1,785 & 1,458 \\
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no DO, NMS & 1,011 & 426 & 425 \\
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\hline
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\end{tabular}
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\caption{Comparison of Bayesian SSD variants without dropout with
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respect to the number of detections before the entropy threshold,
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after it and/or NMS, and after top \(k\). The
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entropy threshold 1.5 was used for both. The numbers are for
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the dogs class.}
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\end{table}
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\end{frame}
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\begin{frame}{Effect of Dropout on Observations}
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\begin{table}[tbp]
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\centering
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\begin{tabular}{rccc}
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\hline
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variant & after & after \\
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& prediction & observation grouping \\
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\hline
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no DO, NMS & 1,677,050 & 155,250 \\
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keep rate 0.9, NMS & 1,617,675 & 549,166 \\
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\hline
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\end{tabular}
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\caption{Comparison of Bayesian SSD variants without dropout and with
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0.9 keep ratio of dropout with
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respect to the number of detections directly after the network
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predictions and after the observation grouping.}
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\end{table}
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\end{frame}
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\begin{frame}{Conclusion}
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\begin{itemize}
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\item hypothesis was not confirmed
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\vfill
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\item worse performance than according to \cite{Miller2018}
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\vfill
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\item reason for difference should be investigated
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\end{itemize}
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\end{frame}
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\begin{frame}{Questions}
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\centering
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Time for questions!
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\end{frame}
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\begin{frame}[t,allowframebreaks]{References}
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\printbibliography
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\end{frame}
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\end{document}
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