add implementation stuff
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\section{Experimental Results}
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					\section{Experimental Results}
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					\begin{figure}[h]
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					    \centering
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					    \includegraphics[width=\linewidth]{../rsc/AUC_normal_lowcer_2_20}
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					    \caption{Architecture convolutional neural network. Image by \href{https://cointelegraph.com/explained/what-are-convolutional-neural-networks}{SKY ENGINE AI}}
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					    \label{fig:cnn-architecture}
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					\end{figure}
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@@ -29,22 +29,30 @@ That means taking the absolute value of the prediction minus the class center re
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\cite{activelearning}
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					\cite{activelearning}
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					With the help of this metric the pseudo predictions can be sorted by the score $S(z)$.
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					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.
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\begin{equation}\label{eq:minnot}
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					\begin{equation}\label{eq:minnot}
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    \text{min}_n(S) \coloneqq a \subset S \mid \text{where a are the n smallest numbers of S}
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					    \text{min}_n(S) \coloneqq a \subset S \mid \text{where } a \text{ are the } n \text{ smallest numbers of } S
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\end{equation}
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					\end{equation}
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\begin{equation}\label{eq:maxnot}
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					\begin{equation}\label{eq:maxnot}
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    \text{max}_n(S) \coloneqq a \subset S \mid \text{where a are the n largest numbers of S}
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					    \text{max}_n(S) \coloneqq a \subset S \mid \text{where } a \text{ are the } n \text{ largest numbers of } S
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\end{equation}
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					\end{equation}
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\subsection{Low certainty first}
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					This notation helps to define which subsets of samples to give the user for labeling.
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					There are different ways how this subset can be chosen.
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					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}.
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					Furthermore, the two mixtures between them, halt-high and half-low certain and only the middle section of the sorted certainty scores.
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					\subsection{Low certainty first}\label{subsec:low-certainty-first}
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We take the samples with the lowest certainty score first and give it to the user for labeling.
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					We take the samples with the lowest certainty score first and give it to the user for labeling.
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\begin{equation}
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					\begin{equation}
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    \text{min}_\mathcal{B}(S(z))
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					    \text{min}_\mathcal{B}(S(z))
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\end{equation}
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					\end{equation}
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\subsection{High certainty first}
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					\subsection{High certainty first}\label{subsec:high-certainty-first}
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We take the samples with the highest certainty score first and give it to the user for labeling.
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					We take the samples with the highest certainty score first and give it to the user for labeling.
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\begin{equation}
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					\begin{equation}
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