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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}} 
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    \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[]{}

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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}

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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}
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\item Learning with dependent data
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\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}
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\item Learning with strong supervision
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\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}
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\begin{block}{\Large Flexible Fine-tuning (Flex-Tuning) {\tiny [Royer, Lampert]}}
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  \input{finetuning.tex}
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\end{block}

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\begin{block}{\Large Multi-exit Distillation {\tiny [Phuong, Lampert]}}
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  \begin{columns}
    \begin{column}{0.62\textwidth}
      \includegraphics[width=\textwidth]{multi-output/architecture.pdf}
    \end{column}
    \begin{column}{0.35\textwidth}
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      \hspace*{-2cm}%
      \includegraphics[width=1.1\textwidth]{multi-output/results_imnet100.png}
      \vspace*{1\baselineskip}
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      \textbf{Multi-exit architectures}
      \begin{itemize}
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      \item can be stopped anytime to \\provide a valid prediction
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      \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}

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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]}}
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\begin{minipage}{.48\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
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\textbf{Goal:}
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\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 $c_1,\dots,c_t$
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\item avoid \emph{catastrophic forgetting}
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\end{itemize}
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\bigskip
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\textbf{Method:}
\begin{itemize}
\item select and store small number of exemplars
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\item train with distillation objective
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\end{itemize}
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\end{minipage}
%
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\begin{minipage}{.48\textwidth}
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%\includegraphics[width=\textwidth]{incremental}
\includegraphics[width=\textwidth]{cifar-cumul10-legend}
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\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, Lampert. ICML 2017]}}
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  \begin{minipage}{.45\textwidth}
\textbf{Situation:}
\begin{itemize}
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\item many learning tasks to solve, \newline most have only unlabeled data
\end{itemize}

\textbf{Goal:}
\begin{itemize}
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\item learn predictors for each task \\(including unlabeled ones)
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\end{itemize}

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\textbf{Method:}
\begin{itemize}
\item share data between tasks
\item derive optimal way to share from generalization bound
\end{itemize}
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\end{minipage}
%
\begin{minipage}{.5\textwidth}
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\includegraphics[width=.9\textwidth]{asya-multitask}\qquad % with-theorem
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\end{minipage}
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\end{block}

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\begin{block}{\Large Conditional Risk Minimization {\tiny [Zimin, Lampert. AISTATS 2017]}}
  \begin{minipage}{.45\textwidth}

\textbf{Situation:}
\begin{itemize}
\item data is stochastic process, $z_1,z_2,\dots$
\end{itemize}

\textbf{Goal:}
\begin{itemize}
\item learn predictor $h$ for next step of process
\end{itemize}

\textbf{Method:}
\begin{itemize}
\item minimize \emph{conditional risk} 
$$\mathcal{R}_{\text{cond}}(h) = \mathbb{E}[\ell(z_{n+1},h) | z_1,\dots,z_n]$$
instead of marginal risk 
$$\mathcal{R}_{\text{marg}}(h) = \mathbb{E}[\ell(z_{n+1},h)]$$
\end{itemize}
\end{minipage}
%
\begin{minipage}{.5\textwidth}
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\includegraphics[width=\textwidth]{timeseries}\qquad
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\end{minipage}

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\end{block}

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\vspace*{5mm}
{\small
Rebuffi, S.A., Kolesnikov, A., Sperl, G. and Lampert, C.H., 2017. iCaRL: Incremental classifier and representation learning. \emph{CVPR}.

Pentina, A. and Lampert, C.H., 2017. Multi-task learning with labeled and unlabeled tasks. \emph{ICML}.

Zimin, A. and Lampert, C., 2017. Learning theory for conditional risk minimization. \emph{AISTATS}.
}
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\end{column}
\end{columns}
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\vspace*{-20mm}

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% [Reference 1][Reference 1][Reference 1][Reference 1][Reference 1][Reference 1][Reference 1][Reference 1][Reference 1][Reference 1][Reference 1][Reference 1][Reference 1][Reference 1]
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\end{frame}


\end{document}