now it compiled

poster.tex

@@ -33,7 +33,7 @@
 %\usepackage{times}\usefonttheme{professionalfonts}  % obsolete
 %\usefonttheme[onlymath]{serif}
 \boldmath
-\usepackage[orientation=portrait,size=a0,scale=1.4, debug]{beamerposter}
+\usepackage[orientation=landscape,size=a1,scale=1.4, debug]{beamerposter}
 % change list indention level
 % \setdefaultleftmargin{3em}{}{}{}{}{}
 
@@ -72,7 +72,7 @@
 
 %\title{\LARGE iCaRL: incremental Classifier and Representation Learning}
 \title{Computer Vision and Machine Learning}
-\author{}
+\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}
@@ -80,7 +80,7 @@
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \newlength{\columnheight}
-\setlength{\columnheight}{95cm}
+\setlength{\columnheight}{40cm}
 \setlength{\columnsep}{1cm}
 
 \renewcommand{\P}{\pmb{\mathbb{P}}}
@@ -139,8 +139,14 @@
 
 \vspace*{-1.5cm}
 
-\begin{block}{\Large People}
-  \newcommand{\peopleheight}{6cm}
+\vskip-1cm
+\begin{columns}[t]
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%% First COlumn
+\ \ \ \begin{column}{.49\textwidth}
+\begin{block}{\Large Our Research}
+
+  \newcommand{\peopleheight}{4cm}
   \begin{center}
     \includegraphics[height=\peopleheight{}]{people/clampert.jpg} ~~
     \includegraphics[height=\peopleheight{}]{people/akolesnikov-new.jpg} ~~
@@ -152,136 +158,59 @@
     \includegraphics[height=\peopleheight{}]{people/gsperl.jpg} ~~
     \includegraphics[height=\peopleheight{}]{people/azimin.jpg} ~~
   \end{center}
-\end{block}
-
-\vskip-1cm
-\begin{columns}[t]
-%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%% First COlumn
-\ \ \ \begin{column}{.49\textwidth}
-\begin{block}{\Large Multi-Task Learning}
 
 \bigskip
 Object categorization methods are trained to recognize \textbf{1000s of classes}:
 
-\medskip
-\centerline{\includegraphics[width=.9\textwidth]{2010-imagenet}}%
-\medskip
-\blue{Standard training requires:}
-\begin{itemize}
-\item random order access to 100s of GB of training data,
-\item many days to weeks of training time.
-\end{itemize}
-
-\bigskip
-
-\medskip
-\centerline{\includegraphics[width=.9\textwidth]{2010-imagenet-new}}%
-\medskip
+\end{block}
 
-\blue{What, if a few new classes are meant to be included?}
-\begin{itemize}
-\item training must be re-run for all classes 
+\begin{block}{\Large Example: Fine-tuning}
 
-$\rightarrow$ huge computational cost, all training data must be kept around {\color{orange}\large \Frowny{}}
-\end{itemize}
+  \begin{center}
+    yes%\includegraphics{dummy} ~~
+  \end{center}
 
-\bigskip
-Potential solution: \bblue{class-incremental learning}
 \end{block}
 
+\begin{block}{\Large Example: Distillation}
 
-\vskip4\blockskip
-\begin{block}{\Large Conditional Risk}
-\textbf{Fixed data representation:}
-\begin{itemize}
-\item retrain classifiers on data subset with biased regularization {\scriptsize [Kuzborskij \etal, 2013]}
-\item represent classes by \blue{mean feature vectors} {\scriptsize [Mensink \etal, 2012], [Ristin \etal, 2014]}
-\end{itemize}
-\textbf{Learning the data representation:}
-\begin{itemize}
-\item grow neural network incrementally, fixing parts that are responsible for earlier class decisions
-{\scriptsize [Mandziuk, Shastri. 1998], \dots, [Rusu \etal, 2016]}
-
-\item continuously generate patterns to prevent forgetting {\scriptsize [Ans, Rousset. 2000]}
+  \begin{center}
+    yes%\includegraphics{dummy} ~~
+  \end{center}
 
-\item multi-task setting: preserve network activations by \blue{distillation} {\scriptsize [Li, Hoiem. 2016]}
-\end{itemize}
 \end{block}
 
-\vskip4\blockskip
-\begin{block}{\Large iCaRL} % {\scriptsize [arXiv \dots]}}
-We incrementally learn \blue{classifiers and features} with a fixed-size network.
+\end{column}
+%
 
-%Notation:
-\begin{itemize}
-\item $f^1(x),\dots,f^T(x)$: probabilistic network outputs for (up to) $T$ classes
-\item $\bphi$: (current) feature representation defined by the network
-%\item $K$: size of extra memory that we can use to store images/exemplars  
-\item $t$: number of classes observed so far
-\item $P^y=(p^y_1,\dots,p^y_{m})$ set of exemplar images for class $y=1,\dots,t$
-\end{itemize}
 
-\bigskip
-\bblue{iCaRL component 1: exemplar-based classification.}
-\begin{itemize}
-\item a new image, $x$, is classified by the \blue{nearest-mean-of-exemplars} rule
-$$ y^\ast = \operatorname*{argmin}_{y=1,\dots,t} \Big\|\bphi(x) - \mu^y\Big\|
-\qquad\text{for }\ 
-\mu^y=\frac{1}{m}\sum_{j=1}^{m}\bphi(p^y_j).$$
-\end{itemize}
+\ \ \begin{column}{.495\textwidth}
 
-\bigskip
-\bblue{iCaRL component 2: representation learning.}
 
-For new data $X^y=\{x^y_1,\dots,x^y_{n_y}\}$ of classes $y=t\!+\!1,\dots,t'$
-\begin{itemize}
-\item create training set of training examples and exemplars
-$$\mathcal{D} \leftarrow \bigcup_{y=t+1,\dots,t'}\!\!\!\{(x,y) : x\in X^y\} 
-\ \cup\!\!\bigcup_{y=1,\dots,t} \!\!\!\{(x,y) : x\in P^y\} $$
-\item for all $x_i\in\mathcal{D}$, store network outputs $a^y_i = f^y(x_i)$ of classes $y=1,\dots,t$ 
-\item update the network parameters, $\theta$, using BackProp on loss function
-$$\ell(\theta) = -\!\!\!\!\!\!\sum_{(x_i,y_i)\in\mathcal{D}}\!\!\!\![\ \log( f^{y_i}(x_i;\theta)+\sum_{y=1}^{t}\, a^y_i\log( f^y(x_i;\theta) )\ ].$$
-\end{itemize}
+\begin{block}{\Large Example: Class-incremental learning}
+
+  \begin{center}
+    yes%\includegraphics{dummy} ~~
+  \end{center}
 
 \bigskip
-\bblue{iCaRL component 3: exemplar selection.}
+Object categorization methods are trained to recognize \textbf{1000s of classes}:
 
-When the number of observed classes increases from $t$ to $t'$: set $m'=\frac{K}{t'}$.
-\begin{itemize}
-\item for classes $y=1,\dots,t$, keep exemplars $p^y_{1},\dots,p^y_{m'}$, discard others % $p^j_{m'_j+1},\dots,p^j_{m_j}$
-\item for classes $y=t\!+\!1,\dots,t'$, find new exemplars $p^y_{1},\dots,p^y_{m'}$ as 
-\begin{align*}
-p^y_k \leftarrow\!\argmin\limits_{x\in X^y} \Big\| \frac{1}{n_y}\sum_{i=1}^{n_y}\!\!\bphi(x^y_i) - \frac{1}{k}\Big[\bphi(x)+\sum_{j=1}^{k-1}\!\!\bphi(p^y_j)\Big] \Big\|
-\ \text{for $k=1,\dots,m'$}.
-\end{align*}
-%\begin{align*}
-%\operatorname*{argmin}_{|I|=m'_j} \Big\|  \frac{1}{n_j}\sum_{i=1}^{n_j}\bphi(x^j_i) - \frac{1}{m'_j}\sum_{i\in I}\bphi(x^j_i) \Big\|
-%\end{align*}
-\end{itemize}
 \end{block}
 
-%
-%
-    %\STATE // form combined training set:
-    %%
-    %\STATE // store network outputs with pre-update parameters:
-    %\FOR{$j=1,\dots,s-1$}
-    %\STATE $a^j_i\leftarrow f^j(x_i)$ \quad for all $(x_i,\cdot)\in\D$
-    %\ENDFOR
-    %%\STATE run network training (\eg BackProp) with loss function
-    %\begin{align*}
-    %\ell(\Theta) = -\!\!\!\!\!\!\sum_{(x_i,y_i)\in\D}&\!\!\!\!\!\!\big[\log( f^{y_i}(x_i))+\sum_{j=1}^{s-1}\, a^j_i\log( f^j(x_i) )\big]
-    %\end{align*}
-    %\textcolor{red}{\textbf{is this the right way to handle EXEMPLARS???}}
 
+\begin{block}{\Large Example: Multi-task Learning}
 
-\end{column}
-%
+  \begin{center}
+    yes%\includegraphics{dummy} ~~
+  \end{center}
 
+\bigskip
+Object categorization methods are trained to recognize \textbf{1000s of classes}:
 
-\ \ \begin{column}{.495\textwidth}
-\begin{block}{\Large Multi-output Distillation}
+\end{block}
+
+\begin{block}{\Large Example: Multi-output Distillation}
 \textbf{Situation:}
 \begin{itemize}
 \item classes appear sequentially (or in batches) % $c_1,c_2,\dots,c_T$
@@ -294,96 +223,8 @@ p^y_k \leftarrow\!\argmin\limits_{x\in X^y} \Big\| \frac{1}{n_y}\sum_{i=1}^{n_y}
 \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}
-
-\bigskip
-\textbf{We do not want to/we cannot:}
-\begin{itemize}
-\item retrain from scratch whenever new classes become available
-\item store all training examples (could be millions)
-\end{itemize}
-\bigskip
-\textbf{The dilemma:}
-\begin{itemize}
-  \item \textcolor{blue}{fixing the data representation}: suboptimal results on new classes. % {\tiny [Tommasi \etal, 2015]}.
-  \item \textcolor{blue}{continuously improving the representation}: classifiers for earlier classes deteriorate over time 
-  ("catastrophic forgetting/interference").{\scriptsize [McCloskey, Cohen. 1989]} % (catastrophic forgetting) {\tiny []}.
-  % Ratcliff, R. (1990) Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions. Psychological Review,97, 285-308
-\end{itemize}
 \end{block}
 
-\vskip4\blockskip
-\begin{block}{\Large Flexible Fine-tuning}
-
-\vskip4\blockskip
-\mbox{
-\parbox{.48\textwidth}{ 
-\!\!\!\textbf{CIFAR-100:}
-\begin{itemize}
-\item 100 classes, in batches of 10
-\item 32-layer ResNet {\scriptsize [He \etal, 2015]}
-\item evaluated by top-1 accuracy 
-\item number of exemplars: 2000
-\end{itemize}
-}
-\parbox{.48\textwidth}{ 
-\textbf{ImageNet ILSVRC 2012 (subset):}
-\begin{itemize}
-\item 100 classes, in batches of 10
-\item 18-layer ResNet {\scriptsize [He \etal, 2015]}
-\item evaluated by top-5 accuracy 
-\item number of exemplars: 2000
-\end{itemize}
-}}
-
-\vskip4\blockskip
-\textbf{Baselines:}
-\begin{itemize}
-\item fixed representation: freeze representation after first batch of classes
-\item finetuning: ordinary NN learning, finetune whenever new classes come in
-\item LwF: \emph{"Learning without Forgetting"} {\scriptsize [Li, Hoiem. 2016]}, use network itself to classify
-%\item LwF+proto: like LwF, but with prototypes used for representation learning
-\item iNCM: like iCaRL, but store all images and classify with true class means
-\end{itemize}
-\end{block}
-
-
-
-\vskip4\blockskip
-\begin{block}{\Large 7) Summary}
-\begin{itemize}
-\item iCaRL learns incrementally with a fixed memory footprint
-\item much better results than baselines, on par with (intractable) iNCM 
-\end{itemize}
-\end{block}
-
-
-\bigskip\hrule\medskip\tiny
-
-%[Thrun \etal, "Learning one more thing", \dots]
-
-[Ans, Rousset. \emph{"Neural networks with a self-refreshing memory: Knowledge transfer in sequential learning tasks without catastrophic forgetting"}, Connection Science 12(1), 2000]
-
-[He, Zhang, Ren, Sun. \emph{"Deep residual learning for image recognition"}. arXiv:1512.03385, 2015]
-
-[Hinton, Vinyals, Dean. \emph{"Distilling the Knowledge in a Neural Network"}, NIPS Workshop on Deep Learning, 2014]
-
-[Kuzborskij, Orabona, Caputo. \emph{"From N to N+1: Multiclass transfer incremental learning"}, CVPR 2013]
-
-[Mandziuk, Shastri. \emph{"Incremental class learning approach and its application to handwritten digit recognition"}, Information Sciences, 2002]
-
-[McCloskey, Cohen. \emph{"Catastrophic interference in connectionist networks: The sequential learning problem"}, The Psychology of Learning and Motivation, 1989]
-
-[Mensink, Verbeek, Perronnin, Csurka. \emph{"Distance-based image classification: Generalizing to new Classes at near-zero cost"}, 2013]
-
-[Li, Hoiem. \emph{"Learning without forgetting"}, ECCV 2016]
-
-[Ristin, Huillaumin, Gall, van Gool. \emph{"Incremental learning of NCM forests for large-scale image classification"}, CVPR 2014]
-
-[Rusu, Rabinowitz, Desjardins, Soyer, Kirkpatrick, Kavukcuoglu, Pascanu, Hadsell. \emph{"Progressive neural networks"}, arXiv:1606.04671 [cs.LG] 2016]
-
-%[Ans, "Sequential Learning in Distributed Neural Networks without Catastrophic Forgetting: A Single and Realistic Self-Refreshing Memory Can Do It", Neural Information Processing--Letters and Reviews. 2004]
-%[He, Zhang, Ren, Sun. Deep residual learning for image recognition. CVPR 2016]
-%[Rocco De Rosa, Thomas Mensink, and Barbara Caputo, "Online Open World Recognition"] arXiv
 \end{column}
 \end{columns}