fix stuff from prof

introduction.typ

@@ -8,12 +8,12 @@ Machine learning helped the field to advance a lot in the past.
 Most of the time the error rate is sub $0.1%$ and therefore plenty of good data and almost no faulty data is available.
 So the train data is heavily unbalanced.~#cite(<parnami2022learningexamplessummaryapproaches>)
 
-PatchCore and EfficientAD are state of the art algorithms trained only on good data and then detect anomalies within unseen (but similar) data.
+PatchCore@patchcorepaper and EfficientAD@efficientADpaper are state of the art algorithms trained only on good data and then detect anomalies within unseen (but similar) data.
 One of their problems is the need of lots of training data and time to train.
 Moreover a slight change of the camera position or the lighting conditions can lead to a mandatory complete retraining of the model.
 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>)
 
-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.
+In this thesis the performance of 3 Few-Shot learning algorithms (ResNet50@resnet, P>M>F@pmfpaper, CAML@caml_paper) 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 <sectionresearchquestions>
@@ -23,10 +23,10 @@ _Should Few-Shot learning be used for anomaly detection tasks?
 How does it compare to well established algorithms such as PatchCore or EfficientAD?_
 
 === How does disbalancing the Shot number affect performance?
-_Does giving the Few-Shot learner more good than bad samples improve the model performance?_
+_Does giving the Few-Shot learner a higher proportion of normal (non-anomalous) samples compared to anomalous samples improve the model's performance?_
 
-=== How do the 3 (ResNet, CAML, P>M>F) methods perform in only detecting the anomaly class?
-_How much does the performance improve by only detecting the presence of an anomaly?
+=== How do the 3 (ResNet, CAML, P>M>F) methods perform in distinguishing between different anomaly types?
+_And how much does the performance improve by only detecting the presence of an anomaly?
 How does it compare to PatchCore and EfficientAD?_
 
 /*#if inwriting [
@@ -38,7 +38,7 @@ How does it compare to PatchCore and EfficientAD?_
 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 PatchCore and EfficientAD.
+The section elaborates on the three selected methods—ResNet50@resnet, P>M>F@pmfpaper, and CAML@caml_paper—while also touching upon well established anomaly detection algorithms such as PatchCore 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.