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fix lots of typos
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Anomaly detection is of essential importance, especially in the industrial and automotive field.
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 and almost no faulty data is available.
Most of the time the error rate is sub $0.1%$ and therefore plenty of good data and almost no faulty data is available.
So the train data is heavily unbalanced.~#cite(<parnami2022learningexamplessummaryapproaches>)
PatchCore and EfficientAD are state of the art algorithms trained only on good data and then detect anomalies within unseen (but similar) data.
@ -20,7 +20,7 @@ Moreover, few-shot learning might be able not only to detect anomalies but also
=== 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 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?_
@ -38,7 +38,7 @@ How does it compare to PatchCore and EfficientAD?_
This thesis is structured to provide a comprehensive exploration of Few-Shot Learning in anomaly detection.
@sectionmaterialandmethods introduces the datasets and methodologies used in this research.
The MVTec AD dataset is discussed in detail as the primary source for benchmarking, along with an overview of the Few-Shot Learning paradigm.
The section elaborates on the three selected methods—ResNet50, P>M>F, and CAML—while also touching upon well established anomaly detection algorithms such as Pachcore and EfficientAD.
The section elaborates on the three selected methods—ResNet50, P>M>F, and CAML—while also touching upon well established anomaly detection algorithms such as PatchCore and EfficientAD.
@sectionimplementation focuses on the practical realization of the methods described in the previous chapter.
It outlines the experimental setup, including the use of Jupyter Notebook for prototyping and testing, and provides a detailed account of how each method was implemented and evaluated.