remove extra from production build
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@ -1,4 +1,4 @@
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#import "utils.typ": todo
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#import "utils.typ": todo, inwriting
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#import "@preview/subpar:0.1.1"
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= Experimental Results <sectionexperimentalresults>
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@ -101,5 +101,7 @@ One could use a well established algorithm like PatchCore or EfficientAD for det
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label: <comparisonnormal>,
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)
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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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#todo[Maybe don't do this]
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]
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#import "utils.typ": todo
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#import "utils.typ": todo, inwriting
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= Introduction
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== Motivation
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@ -29,8 +29,10 @@ Does giving the Few-Shot learner more good than bad samples improve the model pe
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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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