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@ -456,8 +456,8 @@ Liu et al.~\cite{Liu2016}.
\section{Bayesian SSD for Model Uncertainty}
Networks trained with dropout are a general approximate Bayesian model~\cite{Gal2017}. As such, they can be used for everything a true
Bayesian model could be used for. This idea is applied to \gls{SSD} by Miller et al.: two dropout layers are added to \gls{vanilla} \gls{SSD}, after the layers fc6 and fc7 respectively (see figure \ref{fig:bayesian-ssd}).
Networks trained with dropout are a general approximate Bayesian model~\cite{Gal2017} and can be used for everything a true
Bayesian model could be used for. Miller et al. apply this idea to \gls{SSD}: two dropout layers are added to \gls{vanilla} \gls{SSD}, after the layers fc6 and fc7 respectively (see figure \ref{fig:bayesian-ssd}).
\begin{figure}
\centering