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src/introduction.tex

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 \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}
+Anomaly detection has especially in the industrial and automotive field essential importance.
+Lots of assembly lines need visual inspection to find errors often with the help of camera systems.
+Machine learning helped the field to advance a lot in the past.
+PatchCore and EfficientAD are algorithms trained only on good data and then detect anomalies.
+The problem is they need a lot of training data and time to train.
+Few-Shot learning might be a suitable alternative with essentially lowered train time.
+
+In this thesis the performance of 3 Few-Shot learning algorithms will be compared in the field of annomaly detection.
 
 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}