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35 lines
1.9 KiB
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35 lines
1.9 KiB
XML
= Introduction
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== Motivation
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Anomaly detection has especially in the industrial and automotive field essential importance.
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Lots of assembly lines need visual inspection to find errors often with the help of camera systems.
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Machine learning helped the field to advance a lot in the past.
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Most of the time the error rate is sub $.1%$ and therefore plenty of good data is available and the data is heavily unbalaned.
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PatchCore and EfficientAD are state of the art algorithms trained only on good data and then detect anomalies within unseen (but similar) data.
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One of their problems is the need of lots of training data and time to train.
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Moreover a slight change of the camera position or the lighting conditions can lead to a complete retraining of the model.
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Few-Shot learning might be a suitable alternative with hugely lowered train times and fast adaption to new conditions.
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In this thesis the performance of 3 Few-Shot learning algorithms will be compared in the field of anomaly detection.
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Moreover, few-shot learning might be able not only to detect anomalies but also to detect the anomaly class.
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== Research Questions
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=== Is Few-Shot learning a suitable fit for anomaly detection?
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Should Few-Shot learning be used for anomaly detection tasks?
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How does it compare to well established algorithms such as Patchcore or EfficientAD?
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=== How does disbalancing the Shot number affect performance?
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Does giving the Few-Shot learner more good than bad samples improve the model performance?
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=== How does the 3 (ResNet, CAML, \pmf) methods perform in only detecting the anomaly class?
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How much does the performance improve if only detecting an anomaly or not?
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How does it compare to PatchCore and EfficientAD?
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=== Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?
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// I've tried different distance measures $->$ but results are pretty much the same.
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== Outline
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todo
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