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+= Introduction
+== Motivation
+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 state of the art algorithms trained only on good data and then detect anomalies within unseen (but similar) data.
+One of their problems is the need of lots 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 anomaly detection.
+Moreover, few-shot learning might be able not only to detect anomalies but also to detect the anomaly class.
+
+== Research Questions
+
+=== 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?
+
+=== How does disbalancing the Shot number affect performance?
+Does giving the Few-Shot learner more good than bad samples improve the model performance?
+
+=== How does the 3 (ResNet, CAML, \pmf) 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?
+
+=== 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.
+
+== Outline
+todo