...
 
Commits (2)
......@@ -184,6 +184,18 @@ 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}
\includegraphics[width=.5\columnwidth]{incremental}
......@@ -200,18 +212,9 @@ 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}
......