\section{Material and Methods}\label{sec:material-and-methods}

\subsection{Material}\label{subsec:material}

\subsubsection{Dagster}
\subsubsection{Label-Studio}
\subsubsection{Pytorch}
\subsubsection{NVTec}
\subsubsection{Imagenet}

\subsection{Methods}\label{subsec:methods}

\subsubsection{Active-Learning}
\subsubsection{ROC and AUC}
\subsubsection{RESNet}
\subsubsection{CNN}
\subsubsection{Softmax}

The Softmax function converts $n$ numbers of a vector into a probability distribution.
Its a generalization of the Sigmoid function and often used as an Activation Layer in neural networks.
\begin{equation}\label{eq:softmax}
    \sigma(\mathbf{z})_j = \frac{e^{z_j}}{\sum_{k=1}^K e^{z_k}} \; for j\coloneqq\{1,\dots,K\}
\end{equation}