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@@ -659,30 +659,6 @@ from 0.1 to 2.5 as specified in Miller et al.~\cite{Miller2018}.
 
 \label{chap:discussion}
 
-To recap, the hypothesis is repeated here.
-
-\begin{description}
-    \item[Hypothesis] Novelty detection using auto-encoders delivers similar or better object detection performance under open set conditions while being less computationally expensive compared to dropout sampling.
-\end{description}
-
-Based on the reported results, no clear answer can be given to the
-research question; rather new questions emerge: "Can auto-encoders
-work on realistic data sets like COCO with multiple different classes
-in one image?" In other words: "Is my experience due to
-implementation issues or a general theoretical problem of
-auto-encoders?"
-
-Despite best efforts, the results of Miller et al.~\cite{Miller2018}
-could not be replicated. This does not show anything though.
-To disprove Miller's work, any and all possible ways to replicate
-their work must fail. Contrarily, one successful replication
-proves the ability to replicate. On the surface, both Miller et al.
-and I used the same weights, the same network, and the same
-data sets. Only difference of note: they used a Caffe implementation
-of SSD, for this thesis the Tensorflow implementation with eager mode
-was used.
-
-
 
 \chapter{Closing}
 \label{chap:closing}