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-Test intro
+\section{Introduction}\label{sec:introduction}
+\subsection{Motivation}\label{subsec:motivation}
+For most supervised learning tasks lots of training samples are essential.
+With too less training data the model will not generalize well and not fit a real world task.
+Labeling datasets is commonly seen as an expensive task and wants to be avoided as much as possible.\cite{generalAI}
+That's why there is a machine-learning field called active learning.
+The general approach is to train a model that predicts within every iteration a ranking metric or Pseudo-Labels which then can be used to rank the importance of samples to be labeled by an oracle.
+These labeled samples are then used to train the model.\cite{activelearning}
+
+The goal of this practical work is to test active learning within a simple classification task and evaluate its performance.
+\subsection{Research Questions}\label{subsec:research-questions}
+
+\subsubsection{Is Few-Shot learning a suitable fit for anomaly detection?}
+
+Should Few-Shot learning be used for anomaly detection tasks?
+How does it compare to well established algorithms such as Patchcore or EfficientAD?
+
+\subsubsection{How does disbalancing the Shot number affect performance?}
+Does giving the Few-Shot learner more good than bad samples improve the model performance?
+
+\subsubsection{How does the 3 methods perform in only detecting the anomaly class?}
+How much does the performance improve if only detecting an anomaly or not?
+How does it compare to PatchCore and EfficientAD?
+
+\subsubsection{Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?}
+I've tried different distance measures -> but results are pretty much the same.
+
+\subsection{Outline}\label{subsec:outline}
+