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52 lines
3.8 KiB
Typst
52 lines
3.8 KiB
Typst
#import "utils.typ": todo, inwriting
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= 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 and almost no faulty data is available.
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So the train data is heavily unbalaned.~#cite(<parnami2022learningexamplessummaryapproaches>)
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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 mandatory 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.~#cite(<efficientADpaper>)#cite(<patchcorepaper>)#cite(<parnami2022learningexamplessummaryapproaches>)
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In this thesis the performance of 3 Few-Shot learning algorithms (ResNet50, P>M>F, CAML) 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 <sectionresearchquestions>
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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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#if inwriting [
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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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]
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== Outline
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This thesis is structured to provide a comprehensive exploration of Few-Shot Learning in anomaly detection.
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@sectionmaterialandmethods introduces the datasets and methodologies used in this research.
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The MVTec AD dataset is discussed in detail as the primary source for benchmarking, along with an overview of the Few-Shot Learning paradigm.
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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.
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@sectionimplementation focuses on the practical realization of the methods described in the previous chapter.
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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.
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The experimental outcomes are presented in @sectionexperimentalresults.
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This section addresses the research questions posed in @sectionresearchquestions, examining the suitability of Few-Shot Learning for anomaly detection tasks, the impact of class imbalance on model performance, and the comparative effectiveness of the three selected methods.
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//Additional experiments explore the differences between Euclidean distance and Cosine similarity when using ResNet as a feature extractor.#todo[Maybe remove this]
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Finally, @sectionconclusionandoutlook, summarizes the key findings of this study.
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It reflects on the implications of the results for the field of anomaly detection and proposes directions for future research that could address the limitations and enhance the applicability of Few-Shot Learning approaches in this domain.
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