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Made background section about dropout sampling more generalised

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-10-01 13:30:30 +02:00
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@ -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