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lukas-heiligenbrunner 2025-01-07 18:12:08 +01:00
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commit 1a5dc337f7
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#import "utils.typ": todo
#import "utils.typ": todo, inwriting
#import "@preview/subpar:0.1.1"
= Experimental Results <sectionexperimentalresults>
@ -101,5 +101,7 @@ One could use a well established algorithm like PatchCore or EfficientAD for det
label: <comparisonnormal>,
)
#if inwriting [
== Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?
#todo[Maybe don't do this]
]

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#import "utils.typ": todo
#import "utils.typ": todo, inwriting
= Introduction
== Motivation
@ -29,8 +29,10 @@ Does giving the Few-Shot learner more good than bad samples improve the model pe
How much does the performance improve if only detecting an anomaly or not?
How does it compare to PatchCore and EfficientAD?
#if inwriting [
=== 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
This thesis is structured to provide a comprehensive exploration of Few-Shot Learning in anomaly detection.