Written technical contribution

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

body_expose.tex

@@ -409,6 +409,45 @@ and do not have a winning class label of "unknown".
 
 \subsection*{Technical Contribution}
 
+Technical contribution includes all contributions
+that are not necessarily new in the scientific sense but are a
+meaningful engineering contribution in itself.
+
+There is no available source code for the work of
+Miller et al\cite{Miller2018}, which necessitates a re-implementation
+of their work by myself. The contribution is the fine-tuning of
+an SSD model pre-trained on ImageNet\cite{Deng2009}, extended by
+dropout layers, to the SceneNet RGB-D data set using MS COCO classes
+as the known classes for SSD.
+As MS COCO classes are more general than SceneNet RGB-D classes this
+also requires a mapping from one set of classes to the other.
+This entire contribution is technical and only re-implements
+what Miller et al have already done. It is expected that the
+evaluation of the results using this self-trained model will
+reproduce the results of Miller et al.
+
+For GPND source code is available but only for MNIST and using
+PyTorch. Therefore, the source code has to be transcoded from
+PyTorch to Tensorflow. Furthermore, it must be made compatible
+with the SceneNet RGB-D as the architecture is tailored to MNIST.
+The mapping from SceneNet RGB-D to MS COCO applies here as well and
+can therefore be considered a separate contribution. A fine-tuned
+SSD is required also but this time without added dropout layers.
+Additionally, it is necessary to train the auto-encoder for every
+known class separately.
+
+To summarise it in a list, the following separate deliverables
+are contributed:
+
+\begin{itemize}
+    \item source code for dropout sampling compatible with Tensorflow
+    \item source code for GPND compatible with Tensorflow
+    \item mapping from SceneNet RGB-D classes to MS COCO classes
+    \item vanilla SSD model fine-tuned on SceneNet RGB-D
+    \item dropout SSD model fine-tuned on SceneNet RGB-D
+    \item auto-encoder model trained separately on every MS COCO class
+\end{itemize}
+
 \chapter{Thesis as a project}
 
 After introducing the topic and the general task ahead, this part of