add outline

conclusionandoutlook.typ

@@ -1,4 +1,4 @@
-= Conclusion and Outlook
+= Conclusion and Outlook <sectionconclusionandoutlook>
 == Conclusion
 In conclusion one can say that Few-Shot learning is not the best choice for anomaly detection tasks.
 It is hugely outperformed by state of the art algorithms like Patchcore or EfficientAD.

experimentalresults.typ

@@ -1,7 +1,7 @@
 #import "utils.typ": todo
 #import "@preview/subpar:0.1.1"
 
-= Experimental Results
+= Experimental Results <sectionexperimentalresults>
 
 == Is Few-Shot learning a suitable fit for anomaly detection? <expresults2way>
 _Should Few-Shot learning be used for anomaly detection tasks?

implementation.typ

@@ -3,7 +3,7 @@
 #import "utils.typ": todo
 #import "@preview/subpar:0.1.1"
 
-= Implementation
+= Implementation <sectionimplementation>
 The three methods described (ResNet50, CAML, P>M>F) were implemented in a Jupyter notebook and compared to each other.
 
 == Experiments <experiments>

introduction.typ

@@ -15,7 +15,7 @@ Few-Shot learning might be a suitable alternative with hugely lowered train time
 In this thesis the performance of 3 Few-Shot learning algorithms will be compared in the field of anomaly detection.
 Moreover, few-shot learning might be able not only to detect anomalies but also to detect the anomaly class.
 
-== Research Questions
+== Research Questions <sectionresearchquestions>
 
 === Is Few-Shot learning a suitable fit for anomaly detection?
 
@@ -33,4 +33,17 @@ How does it compare to PatchCore and EfficientAD?
 // I've tried different distance measures $->$ but results are pretty much the same.
 
 == Outline
-#todo[Todo]
+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.
+
+@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.
+
+The experimental outcomes are presented in @sectionexperimentalresults.
+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.
+Additional experiments explore the differences between Euclidean distance and Cosine similarity when using ResNet as a feature extractor.#todo[Maybe remove this]
+
+Finally, @sectionconclusionandoutlook, summarizes the key findings of this study.
+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.

materialandmethods.typ

@@ -2,7 +2,7 @@
 #import "utils.typ": todo
 #import "@preview/equate:0.2.1": equate
 
-= Material and Methods
+= Material and Methods <sectionmaterialandmethods>
 
 == Material