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\boldmath
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\usepackage[orientation=landscape,size=a1,scale=1.4, debug]{beamerposter}
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%\title{\LARGE iCaRL: incremental Classifier and Representation Learning}
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\title{Computer Vision and Machine Learning}
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\author{~}
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\institute{\vskip-.5\baselineskip\large Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria}
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%\institute{~}%Christoph Lampert} %\textsuperscript{1} ENS Rennes (Ecole Normale Sup\'{e}rieure de Rennes), Rennes, France \textsuperscript{2} IST Austria (Institute of Science and Technology Austria), Klosterneuburg, Austria}
%\date[]{}

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\setlength{\columnheight}{40cm}
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\begin{document}
\begin{frame}

\vspace*{-1.5cm}

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\vskip-1cm
\begin{columns}[t]
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% First COlumn
\ \ \ \begin{column}{.49\textwidth}
\begin{block}{\Large Our Research}

  \newcommand{\peopleheight}{4cm}
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  \begin{center}
    \includegraphics[height=\peopleheight{}]{people/clampert.jpg} ~~
    \includegraphics[height=\peopleheight{}]{people/akolesnikov-new.jpg} ~~
    \includegraphics[height=\peopleheight{}]{people/nkonstan.jpg} ~~
    \includegraphics[height=\peopleheight{}]{people/apentina-new.jpg} ~~
    \includegraphics[height=\peopleheight{}]{people/bphuong.jpg} ~~
    \includegraphics[height=\peopleheight{}]{people/srebuffi.jpg} ~~
    \includegraphics[height=\peopleheight{}]{people/aroyer.jpg} ~~
    \includegraphics[height=\peopleheight{}]{people/gsperl.jpg} ~~
    \includegraphics[height=\peopleheight{}]{people/azimin.jpg} ~~
  \end{center}
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\bigskip
Object categorization methods are trained to recognize \textbf{1000s of classes}:

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\end{block}
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\begin{block}{\Large Example: Fine-tuning}
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  \input{finetuning.tex}
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\end{block}

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\begin{block}{\Large Example: Distillation}
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  \begin{center}
    yes%\includegraphics{dummy} ~~
  \end{center}
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\end{block}

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\end{column}
%
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\ \ \begin{column}{.495\textwidth}
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\begin{block}{\Large Example: Class-incremental learning}
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\textbf{Situation:}
\begin{itemize}
\item classes appear sequentially (or in batches) % $c_1,c_2,\dots,c_T$
\item each class $y$ has training images $X^y=\{x^y_1,\dots,x^y_{n_y}\}$
\end{itemize}

\bigskip
\textbf{We want to/we can:}
\begin{itemize}
\item for any number of observed classes, $t$, learn a multi-class classifier% for $c_1,\dots,c_t$
\item store a certain number, $K$, of images (a few hundreds or thousands)
\end{itemize}
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  \begin{center}
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    \includegraphics[width=.5\columnwidth]{incremental}
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  \end{center}
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\end{block}


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\begin{block}{\Large Example: Multi-task Learning}
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  \begin{center}
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    \includegraphics[width=.5\columnwidth]{asya-multitask-with-theorem}
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  \end{center}
\end{block}

\begin{block}{\Large Example: Multi-output Distillation}
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  \begin{center}
  \includegraphics{multi-output/architecture.pdf}
  \end{center}
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\end{block}

\end{column}
\end{columns}
 
\end{frame}


\end{document}