modify Poster skeleton

poster.tex

@@ -71,9 +71,9 @@
 \setbeamertemplate{itemize item}[default]
 
 %\title{\LARGE iCaRL: incremental Classifier and Representation Learning}
-\title{Incremental Classifier and Representation Learning}
-\author{\large Sylvestre-Alvise Rebuffi$^{\dag,*}$, Alexander Kolesnikov$^{*}$, Christoph H. Lampert$^{*}$}
-\institute{\vskip-.5\baselineskip\large $^{\dag}$ CentraleSup\'elec\qquad $^{*}$ IST Austria}
+\title{Computer Vision and Machine Learning}
+\author{}
+\institute{\vskip-.5\baselineskip\large Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria}
 
 %\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[]{}
@@ -139,17 +139,19 @@
 
 \vspace*{-1.5cm}
 
-\ \ \begin{block}{\Large Abstract}
-%\large 
-We introduce \bblue{iCaRL}, a method for simultaneously learning classifiers 
-and a feature representation from training data in which classes 
-occur incrementally. 
-
-iCaRL uses a \blue{nearest-mean-of-exemplars} classifier, \blue{herding for 
-adaptive exemplar selection} and \blue{distillation for representation learning
-without catastrophic forgetting}.
-%
-Experiments on CIFAR and ILSVRC\,2012 show that iCaRL can learn  incrementally over a long period of time where other methods quickly fail.
+\begin{block}{\Large People}
+  \newcommand{\peopleheight}{6cm}
+  \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}
 \end{block}
 
 \vskip-1cm
@@ -157,7 +159,7 @@ Experiments on CIFAR and ILSVRC\,2012 show that iCaRL can learn  incrementally o
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%% First COlumn
 \ \ \ \begin{column}{.49\textwidth}
-\begin{block}{\Large 1) Motivation}
+\begin{block}{\Large Multi-Task Learning}
 
 \bigskip
 Object categorization methods are trained to recognize \textbf{1000s of classes}:
@@ -188,8 +190,9 @@ $\rightarrow$ huge computational cost, all training data must be kept around {\c
 Potential solution: \bblue{class-incremental learning}
 \end{block}
 
+
 \vskip4\blockskip
-\begin{block}{\Large 3) Existing Approaches}
+\begin{block}{\Large Conditional Risk}
 \textbf{Fixed data representation:}
 \begin{itemize}
 \item retrain classifiers on data subset with biased regularization {\scriptsize [Kuzborskij \etal, 2013]}
@@ -207,7 +210,7 @@ Potential solution: \bblue{class-incremental learning}
 \end{block}
 
 \vskip4\blockskip
-\begin{block}{\Large 4) iCaRL} % {\scriptsize [arXiv \dots]}}
+\begin{block}{\Large iCaRL} % {\scriptsize [arXiv \dots]}}
 We incrementally learn \blue{classifiers and features} with a fixed-size network.
 
 %Notation:
@@ -275,8 +278,10 @@ p^y_k \leftarrow\!\argmin\limits_{x\in X^y} \Big\| \frac{1}{n_y}\sum_{i=1}^{n_y}
 
 \end{column}
 %
+
+
 \ \ \begin{column}{.495\textwidth}
-\begin{block}{\Large 2) Class-Incremental Learning}
+\begin{block}{\Large Multi-output Distillation}
 \textbf{Situation:}
 \begin{itemize}
 \item classes appear sequentially (or in batches) % $c_1,c_2,\dots,c_T$
@@ -307,7 +312,7 @@ p^y_k \leftarrow\!\argmin\limits_{x\in X^y} \Big\| \frac{1}{n_y}\sum_{i=1}^{n_y}
 \end{block}
 
 \vskip4\blockskip
-\begin{block}{\Large 5) Experiments (excerpt)}
+\begin{block}{\Large Flexible Fine-tuning}
 
 \vskip4\blockskip
 \mbox{
@@ -341,20 +346,7 @@ p^y_k \leftarrow\!\argmin\limits_{x\in X^y} \Big\| \frac{1}{n_y}\sum_{i=1}^{n_y}
 \end{itemize}
 \end{block}
 
-\vskip4\blockskip
-\begin{block}{\Large 6) Results (excerpt)}
-  \centerline{\qquad\qquad\qquad\textbf{CIFAR-100}\qquad\hfill\textbf{ImageNet ILSVRC~2012}\quad\ \ }
-  \centerline{\includegraphics[height=.3\textwidth]{cifar-cumul10-legend}\includegraphics[height=.3\textwidth]{imagenet-cumul10_top5}}
 
-\medskip
-\textbf{Discussion:}
-\begin{itemize}
-\item as expected: fixed representation and finetuning do not work well 
-\item iCaRL is able to keep good classification accuracy for many iterations  
-\item \emph{"Learning without Forgetting"} starts to forget earlier %(even with prototypes)  
-\item mean-of-exemplars on par with (intractable) iNCM
-\end{itemize}
-    \end{block}
 
 \vskip4\blockskip
 \begin{block}{\Large 7) Summary}
@@ -364,6 +356,7 @@ p^y_k \leftarrow\!\argmin\limits_{x\in X^y} \Big\| \frac{1}{n_y}\sum_{i=1}^{n_y}
 \end{itemize}
 \end{block}
 
+
 \bigskip\hrule\medskip\tiny
 
 %[Thrun \etal, "Learning one more thing", \dots]