bachelor-thesis/introduction.typ

= 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.
Most of the time the error rate is sub $.1%$ and therefore plenty of good data is available and the data is heavily unbalaned.

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.
Moreover a slight change of the camera position or the lighting conditions can lead to a complete retraining of the model.
Few-Shot learning might be a suitable alternative with hugely lowered train times and fast adaption to new conditions.

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