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Bui Thi Mai PHUONG
ard_poster
Commits
e436551d
Commit
e436551d
authored
Oct 02, 2018
by
Mary Phuong
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multi-output image
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e436551d
...
...
@@ -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}
...
...
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