multi-output image

poster.tex

@@ -188,6 +188,18 @@ Object categorization methods are trained to recognize \textbf{1000s of classes}
 
 
 \begin{block}{\Large Example: Class-incremental learning}
+\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}
 
   \begin{center}
     yes%\includegraphics{dummy} ~~
@@ -211,18 +223,9 @@ Object categorization methods are trained to recognize \textbf{1000s of classes}
 \end{block}
 
 \begin{block}{\Large Example: Multi-output Distillation}
-\textbf{Situation:}
-\begin{itemize}
-\item classes appear sequentially (or in batches) % $c_1,c_2,\dots,c_T$
-\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}
+  \begin{center}
+  \includegraphics{multi-output/architecture.pdf}
+  \end{center}
 \end{block}
 
 \end{column}