diff --git a/body_expose.tex b/body_expose.tex
index ca94992..03233fb 100644
--- a/body_expose.tex
+++ b/body_expose.tex
@@ -117,10 +117,7 @@ be implemented in a performant way. Miller et al have shown
 some success in using dropout sampling. However, the many forward
 passes during testing for every image seem computationally expensive.
 In comparison a single run through a trained auto-encoder seems
-intuitively to be faster. This leads to the following hypothesis:
-\emph{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}.
+intuitively to be faster. This leads to the hypothesis (see below).
 
 For the purpose of this thesis, I will
 use the work of Miller et al as baseline to compare against.
@@ -133,6 +130,12 @@ using SSD for object detection and the SceneNet RGB-D data set,
 being equal. With respect to auto-encoders a recent implementation
 of an adversarial auto-encoder\cite{Pidhorskyi2018} will be used.
 
+\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.
+
+\paragraph{Contribution}
 The contribution of this thesis is a comparison between dropout
 sampling and auto-encoding with respect to the overall performance
 of both for object detection in the open-set conditions using