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\documentclass[final]{beamer}
\usepackage{grffile}
\mode<presentation>{\usetheme{IST}}
\usepackage[english]{babel}
\usepackage{setspace}
%\usepackage[latin1]{inputenc}
%\usepackage[T1]{fontenc}
\usepackage[scaled]{helvet}
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\usepackage{pbox}

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\usepackage{framed}
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\def\FrameCommand{\fcolorbox{framecolor}{shadecolor}}%
\MakeFramed {\FrameRestore}}%
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%\usepackage{rotating}
%\usepackage[nointegrals]{wasysym} % for \smiley, \frownie
\usepackage{marvosym}  % \Smiley{} \Frowny{}

\usepackage{pifont}
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\usepackage{amsmath,amsthm, amssymb, latexsym}
%\usepackage{times}\usefonttheme{professionalfonts}  % obsolete
%\usefonttheme[onlymath]{serif}
\boldmath
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\usepackage[orientation=landscape,size=a1,scale=1.4, debug]{beamerposter}
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% change list indention level
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%\newcommand{\pphantom}{\textcolor{lightgray}} % phantom introduces a vertical space in p formatted table columns??!!

\usepackage{graphicx}
\usepackage{subfigure}
\usepackage{amsmath,amssymb,stmaryrd}
%\usepackage{hyperref}
%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\graphicspath{{images/}}
\setbeamertemplate{itemize item}[default]

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  \newlength{\peopleheight}
  \setlength{\peopleheight}{4cm}
  \newlength{\peoplewidth}
  \setlength{\peoplewidth}{4.5cm}

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%\title{\LARGE iCaRL: incremental Classifier and Representation Learning}
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\title{\veryHuge Computer Vision and Machine Learning Group}
\author{\parbox{3.4\peoplewidth}{Members and Alumni:}\small\parbox{\peoplewidth}{\includegraphics[height=\peopleheight{}]{people/clampert.jpg}} 
    \parbox{\peoplewidth}{\includegraphics[height=\peopleheight{}]{people/akolesnikov-new.jpg}} 
    \parbox{\peoplewidth}{\includegraphics[height=\peopleheight{}]{people/nkonstan.jpg}} 
    \parbox{\peoplewidth}{\includegraphics[height=\peopleheight{}]{people/apentina-new.jpg}} 
    \parbox{\peoplewidth}{\includegraphics[height=\peopleheight{}]{people/bphuong.jpg}} 
    \parbox{\peoplewidth}{\includegraphics[height=\peopleheight{}]{people/srebuffi.jpg}} 
    \parbox{\peoplewidth}{\includegraphics[height=\peopleheight{}]{people/aroyer.jpg}} 
    \parbox{\peoplewidth}{\includegraphics[height=\peopleheight{}]{people/gsperl.jpg}} 
    \parbox{\peoplewidth}{\includegraphics[height=\peopleheight{}]{people/azimin.jpg}} 
    \\[.5ex]
\parbox{3.4\peoplewidth}{~}\parbox{\peoplewidth}{Christoph} 
    \parbox{\peoplewidth}{Alex} 
    \parbox{\peoplewidth}{Nikola} 
    \parbox{\peoplewidth}{Asya} 
    \parbox{\peoplewidth}{Mary} 
    \parbox{\peoplewidth}{Sylvestre} 
    \parbox{\peoplewidth}{Am\'elie} 
    \parbox{\peoplewidth}{Georg} 
    \parbox{\peoplewidth}{Alex}
    \\
\parbox{3.4\peoplewidth}{~}\parbox{\peoplewidth}{Lampert} 
    \parbox{\peoplewidth}{Kolesnikov} 
    \parbox{\peoplewidth}{Konstantinov} 
    \parbox{\peoplewidth}{Pentina} 
    \parbox{\peoplewidth}{Phuong} 
    \parbox{\peoplewidth}{Rebuffi}  
    \parbox{\peoplewidth}{Royer} 
    \parbox{\peoplewidth}{Sperl} 
    \parbox{\peoplewidth}{Zimin}}
\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[]{}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\newlength{\columnheight}
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\setlength{\columnheight}{40cm}
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\newcommand<>{\bblue}[1]{\textcolor#2{blue}{\bf #1}}

\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}

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\definecolor{shadecolor}{rgb}{0.7,0.7,0.7}%

%\begin{fshaded}
\textbf{Theory (Statistical Machine Learning)}

\begin{columns}
\begin{column}{.25\textwidth}
\begin{itemize}
\item {Multi-task learning}
\end{itemize}
\end{column}
%
\begin{column}{.27\textwidth}
\begin{itemize}
\item Lifelong learning
\end{itemize}
\end{column}
%
\begin{column}{.35\textwidth}
\begin{itemize}
\item Learning with Strong Supervision
\end{itemize}
\end{column}
\end{columns}

%\end{fshaded}
\vspace{.5\baselineskip}

%\begin{fshaded}
\textbf{Models/Algorithms}

%
\begin{columns}
\begin{column}{.25\textwidth}
\begin{itemize}
\item {Incremental learning}
\end{itemize}
\end{column}
\begin{column}{.27\textwidth}
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\begin{itemize}
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\item Multi-stage architectures
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\end{itemize}
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\end{column}
\begin{column}{.35\textwidth}
\begin{itemize}
\item Non-standard forms of supervision
\end{itemize}
\end{column}
\end{columns}
%\end{fshaded}

%\begin{fshaded}
\vspace{.5\baselineskip}

\textbf{Applications (in Computer Vision)}
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\begin{columns}
\begin{column}{.25\textwidth}
\begin{itemize}
\item {Object recognition}
\end{itemize}
\end{column}
\begin{column}{.27\textwidth}
\begin{itemize}
\item {Object detection}
\end{itemize}
\end{column}
\begin{column}{.35\textwidth}
\begin{itemize}
\item {Semantic image representations}
\end{itemize}
\end{column}
\end{columns}
%\end{fshaded}
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\end{block}
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\vspace{1\baselineskip}
\begin{block}{\Large Flexible Fine-tuning (Flex-Tuning)}
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  \input{finetuning.tex}
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\end{block}

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\begin{block}{\Large Conditional Risk Minimization}
  \begin{minipage}{.45\textwidth}
\textbf{Situation:}
\begin{itemize}
\item data for more and more classes appears sequentially % $c_1,c_2,\dots,c_T$
\end{itemize}
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\end{minipage}
%
\begin{minipage}{.5\textwidth}
\includegraphics[width=\textwidth]{lifelong}\qquad
\end{minipage}
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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 iCaRL (Incremental Classifier and Representation Learning) {\tiny [Rebuffi et al, CVPR 2017]}}
\begin{minipage}{.38\textwidth}
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\textbf{Situation:}
\begin{itemize}
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\item data for more and more classes appears sequentially % $c_1,c_2,\dots,c_T$
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\end{itemize}

\bigskip
\textbf{We want to/we can:}
\begin{itemize}
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\item learn a multi-class classifier for all classes so far % $c_1,c_2,\dots,c_T$% for $c_1,\dots,c_t$
\item avoid \textbf{catastrophic forgetting}
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\item store a certain number, $K$, of images (a few hundreds or thousands)
\end{itemize}
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\bigskip
\textbf{Suggestion: }
\end{minipage}
%
\begin{minipage}{.58\textwidth}
\includegraphics[width=\textwidth]{incremental}
\end{minipage}
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\end{block}


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\begin{block}{\Large Multi-task Learning with Labeled and Unlabeled Tasks {\tiny [Pentina et al, ICML 2017]}}
  \begin{minipage}{.45\textwidth}
\textbf{Situation:}
\begin{itemize}
\item data for more and more classes appears sequentially % $c_1,c_2,\dots,c_T$
\end{itemize}

\end{minipage}
%
\begin{minipage}{.5\textwidth}
\includegraphics[width=\textwidth]{asya-multitask}\qquad
\end{minipage}
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\end{block}

\begin{block}{\Large Example: Multi-output Distillation}
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  \begin{columns}
    \begin{column}{0.68\textwidth}
      \includegraphics{multi-output/architecture.pdf}
    \end{column}
    \begin{column}{0.28\textwidth}
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      \includegraphics[width=0.9\textwidth,height=.6\textwidth]{fine-tuning/selection_criterion_pacs.png}
      
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      \textbf{Multi-exit architectures}
      \begin{itemize}
      \item can be stopped anytime to provide a valid prediction
      \end{itemize}
      \textbf{Proposed training}
      \begin{itemize}
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      \item Distill from later (more accurate) to earlier exits
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      \end{itemize}
    \end{column}
  \end{columns}
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\end{block}

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


\end{document}