Written the scientific contribution

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

body_expose.tex

@@ -333,6 +333,68 @@ it on the MNIST\cite{Lecun1998} data set, once for every digit.
 
 \section{Contribution}
 
+This section will outline what exactly the scientific as well as
+technical contribution of this thesis will be.
+
+\subsection*{Scientific Contribution}
+
+Miller et al\cite{Miller2018} use the SSD\cite{Liu2016} network
+extended with dropout layers and run multiple forward passes
+during the testing phase for every image. Considering the number
+of images in the SceneNet RGB-D\cite{McCormac2017} data set, these
+forward passes will take considerable time. It could be faster
+to only run one forward pass and then use the auto-encoder for
+novelty detection. However, the auto-encoder can only work
+with one detection at the time and must be called for every
+detection of the object detector separately. Therefore,
+it is interesting to investigate whether the second approach
+is indeed faster than the first.
+
+Dropout sampling uses the entropy to identify false positive
+cases. Such identified detections are discarded, which allows for
+a better object detection performance. The GPND approach uses
+the auto-encoder losses and results to identify novel cases and
+therefore mark detections as false positive. Subsequently these
+detections can be discarded as well. By comparing the object
+detection performance after discarding the identified false
+positive cases, the effectiveness of both approaches can be
+compared with each other. It is interesting to research if the
+GPND approach results in a better object detection performance
+than the dropout sampling provides.
+
+The formulated hypothesis, which is repeated after this paragraph,
+combines both aspects and requires a similar or better result in
+both of them. As a consequence it will be falsified if
+the computational performance of the GPND approach is not better than
+the one of dropout sampling or if the object detection performance
+is worse.
+
+\paragraph{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.\\
+
+There are three possible scenarios that can be the result of
+the thesis:
+\begin{itemize}
+    \item the hypothesis is confirmed: Win-Win situation where
+    switching to GPND is straightforward.
+    \item one of the conditions fails: Win-Lose situation where
+    it is a trade-off between object detection performance and
+    computational performance. One approach will be better in
+    one thing and the other approach in the other thing.
+    \item both conditions fail: Lose-Lose situation where
+    dropout sampling is the best in both aspects.
+\end{itemize}
+
+Summarising, the scientific contribution is a comparison between
+dropout sampling and GPND with respect to both object detection
+performance and computational performance under open-set conditions
+using the SceneNet RGB-D data set with the MS COCO classes as
+"known" object classes.
+
+\subsection*{Technical Contribution}
+
 \chapter{Thesis as a project}
 
 After introducing the topic and the general task ahead, this part of