diff --git a/body.tex b/body.tex
index 6286f65..d326f1b 100644
--- a/body.tex
+++ b/body.tex
@@ -44,7 +44,7 @@ class of any given input. In this thesis, I will work with both.
 \begin{figure}
     \centering
     \includegraphics[scale=1.0]{open-set}
-    \caption{Open set problem: The test set contains classes that
+    \caption{Open set problem: the test set contains classes that
     were not present during training time.
     Icons in this image have been taken from the COCO data set
     website (\url{https://cocodataset.org/\#explore}) and were
@@ -103,7 +103,7 @@ representation of the input and has to find a decompression
 that reconstructs the input as accurate as possible. During
 training these auto-encoders learn to reproduce a certain group
 of object classes. The actual novelty detection takes place
-during testing: Given an image, and the output and loss of the
+during testing: given an image, and the output and loss of the
 auto-encoder, a novelty score is calculated. For some novelty
 detection approaches the reconstruction loss is exactly the novelty
 score, others consider more factors. A low novelty
@@ -259,7 +259,7 @@ can be gained with a technique named Monte Carlo Batch Normalisation (MCBN).
 Consequently, this technique can be applied to any network that utilises
 standard batch normalisation.
 Li et al.~\cite{Li2019} investigated the problem of poor performance
-when combining dropout and batch normalisation: Dropout shifts the variance
+when combining dropout and batch normalisation: dropout shifts the variance
 of a neural unit when switching from train to test, batch normalisation
 does not change the variance. This inconsistency leads to a variance shift which
 can have a larger or smaller impact based on the network used.