Made background section about dropout sampling more generalised

Signed-off-by: Jim Martens <github@2martens.de>

body.tex

@@ -384,7 +384,7 @@ the \gls{entropy} \(H(\mathbf{q}) = - \sum_i q_i \cdot \log q_i\).
 
 Miller et al.~\cite{Miller2018} apply the dropout sampling to
 object detection. In that case \(\mathbf{W}\) represents the
-learned weights of a detection network like \gls{SSD}~\cite{Liu2016}.
+learned weights of a detection network, for example \gls{SSD}~\cite{Liu2016}.
 Every forward pass uses a different network
 \(\widetilde{\mathbf{W}}\) which is approximately sampled from
 \(p(\mathbf{W}|\mathbf{T})\). Each forward pass in object