diff --git a/body_expose.tex b/body_expose.tex
index 39cc9b1..7e5d555 100644
--- a/body_expose.tex
+++ b/body_expose.tex
@@ -331,6 +331,35 @@ Practically, the auto-encoder is trained separately for every
 object class that is considered "known". Pidhorskyi et al trained
 it on the MNIST\cite{Lecun1998} data set, once for every digit.
 
+For this thesis it needs to be trained on the SceneNet RGB-D
+data set using MS COCO classes as known classes. As in every
+test epoch all known classes are present, it becomes
+non-trivial which of the trained auto-encoders should be used to
+calculate novelty. To phrase it differently, a true positive
+detection is possible for multiple classes in the same image.
+If, for example, one object is classified correctly by SSD as a chair
+the novelty score should be low. But the auto-encoders of all
+known classes but the "chair" class will give ideally a high novelty
+score. Which of the values should be used? The only sensible solution
+is to only run it through the auto-encoder that was trained for
+the class the SSD model predicted. This provides the following
+scenarios:
+\begin{itemize}
+    \item true positive classification: novelty score should be low
+    \item false positive classification and correct class is
+    among the known classes: novelty score should be high
+    \item false positive classification and correct class is unknown:
+    novelty score should be high
+\end{itemize}
+\noindent
+Negative classifications are not listed as these are not part
+of the output of the SSD and cannot be given to the auto-encoder
+as input. Furthermore, the 2nd case should not happen because
+the trained SSD knows this other class and is very likely
+to give it a higher probability. Therefore, using only one
+auto-encoder fulfils the task of differentiating between
+known and unknown classes.
+
 \section{Contribution}
 
 This section will outline what exactly the scientific as well as
@@ -356,11 +385,11 @@ 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.
+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