diff --git a/rsc/AUC_normal_lowcer_2_20.png b/rsc/AUC_normal_lowcer_2_20.png
new file mode 100644
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diff --git a/src/experimentalresults.tex b/src/experimentalresults.tex
index d546e8e..5d344af 100644
--- a/src/experimentalresults.tex
+++ b/src/experimentalresults.tex
@@ -1 +1,8 @@
-\section{Experimental Results}
\ No newline at end of file
+\section{Experimental Results}
+
+\begin{figure}[h]
+    \centering
+    \includegraphics[width=\linewidth]{../rsc/AUC_normal_lowcer_2_20}
+    \caption{Architecture convolutional neural network. Image by \href{https://cointelegraph.com/explained/what-are-convolutional-neural-networks}{SKY ENGINE AI}}
+    \label{fig:cnn-architecture}
+\end{figure}
\ No newline at end of file
diff --git a/src/implementation.tex b/src/implementation.tex
index a015ecd..c45a063 100644
--- a/src/implementation.tex
+++ b/src/implementation.tex
@@ -29,22 +29,30 @@ That means taking the absolute value of the prediction minus the class center re
 
 \cite{activelearning}
 
+With the help of this metric the pseudo predictions can be sorted by the score $S(z)$.
+We define $\text{min}_n(S)$ and $\text{max}_n(S)$ respectively in~\ref{eq:minnot} and~\ref{eq:maxnot} to define a short form of taking a subsection of the minimum or maximum of a set.
+
 \begin{equation}\label{eq:minnot}
-    \text{min}_n(S) \coloneqq a \subset S \mid \text{where a are the n smallest numbers of S}
+    \text{min}_n(S) \coloneqq a \subset S \mid \text{where } a \text{ are the } n \text{ smallest numbers of } S
 \end{equation}
 
 \begin{equation}\label{eq:maxnot}
-    \text{max}_n(S) \coloneqq a \subset S \mid \text{where a are the n largest numbers of S}
+    \text{max}_n(S) \coloneqq a \subset S \mid \text{where } a \text{ are the } n \text{ largest numbers of } S
 \end{equation}
 
-\subsection{Low certainty first}
+This notation helps to define which subsets of samples to give the user for labeling.
+There are different ways how this subset can be chosen.
+In this PW we do the obvious experiments with High-Certainty first~\ref{subsec:low-certainty-first}, Low-Certainty first~\ref{subsec:high-certainty-first}.
+Furthermore, the two mixtures between them, halt-high and half-low certain and only the middle section of the sorted certainty scores.
+
+\subsection{Low certainty first}\label{subsec:low-certainty-first}
 We take the samples with the lowest certainty score first and give it to the user for labeling.
 
 \begin{equation}
     \text{min}_\mathcal{B}(S(z))
 \end{equation}
 
-\subsection{High certainty first}
+\subsection{High certainty first}\label{subsec:high-certainty-first}
 We take the samples with the highest certainty score first and give it to the user for labeling.
 
 \begin{equation}