fix more comma errors

conclusionandoutlook.typ

@@ -14,7 +14,7 @@ In most of the tests P>M>F performed the best.
 But also the simple ResNet50 method performed better than expected in most cases and can be considered if the computational resources are limited and if a simple architecture is enough.
 
 == Outlook
-In the future when new Few-Shot learning methods evolve it could be interesting to test again how they perform in anomaly detection tasks.
+In the future, when new Few-Shot learning methods evolve, it could be interesting to test again how they perform in anomaly detection tasks.
 There might be a lack of research in the area where the classes to detect are very similar to each other
 and when building a few-shot learning algorithm tailored specifically for very similar classes this could boost the performance by a large margin.
 

experimentalresults.typ

@@ -14,7 +14,7 @@ Both are trained with samples from the 'good' class only.
 So there is a clear performance gap between Few-Shot learning and the state of the art anomaly detection algorithms.
 In the @comparison2way Patchcore and EfficientAD are not included as they aren't directly compareable in the same fashion.
 
-That means if the goal is just to detect anomalies, Few-Shot learning is not the best choice and Patchcore or EfficientAD should be used.
+That means if the goal is just to detect anomalies, Few-Shot learning is not the best choice, and Patchcore or EfficientAD should be used.
 
 #subpar.grid(
   figure(image("rsc/comparison-2way-bottle.png"), caption: [
@@ -97,7 +97,7 @@ One could use a well established algorithm like PatchCore or EfficientAD for det
     8-Way - Cable class
   ]), <comparisonfaultyonlycable>,
   columns: (1fr, 1fr),
-  caption: [Nomaly class only  classification performance],
+  caption: [Anomaly class only  classification performance],
   label: <comparisonnormal>,
 )
 

implementation.typ

@@ -92,7 +92,7 @@ After creating the embeddings for the support and query set the euclidean distan
 The class with the smallest distance is chosen as the predicted class.
 
 === Results <resnet50perf>
-This method performed better than expected wich such a simple method.
+This method performed better than expected with such a simple method.
 As in @resnet50bottleperfa with a normal 5 shot / 4 way classification the model achieved an accuracy of 75%.
 When detecting if there occured an anomaly or not only the performance is significantly better and peaks at 81% with 5 shots / 2 ways.
 Interestintly the model performed slightly better with fewer shots in this case.
@@ -136,7 +136,7 @@ but this is expected as the cable class consists of 8 faulty classes.
 
 == P>M>F
 === Approach
-For P>M>F I used the pretrained model weights from the original paper.
+For P>M>F, I used the pretrained model weights from the original paper.
 As backbone feature extractor a DINO model is used, which is pre-trained by facebook.
 This is a vision transformer with a patch size of 16 and 12 attention heads learned in a self-supervised fashion.
 This feature extractor was meta-trained with 10 public image dasets #footnote[ImageNet-1k, Omniglot, FGVC-
@@ -144,7 +144,7 @@ Aircraft, CUB-200-2011, Describable Textures, QuickDraw,
 FGVCx Fungi, VGG Flower, Traffic Signs and MSCOCO~@pmfpaper]
  of diverse domains  by the authors of the original paper.~@pmfpaper
 
-Finally, this model is finetuned with the support set of every test iteration.
+Finally, this model is fine-tuned with the support set of every test iteration.
 Every time the support set changes, we need to finetune the model again.
 In a real world scenario this should not be the case because the support set is fixed and only the query set changes.
 
@@ -196,12 +196,12 @@ This transformer was trained on a huge number of images as described in @CAML.
 
 === Results
 The results were not as good as expeced.
-This might be caused by the fact that the model was not fine-tuned for any industrial dataset domain.
+This might be because the model was not  fine-tuned for any industrial dataset domain.
 The model was trained on a large number of general purpose images and is not fine-tuned at all.
 Moreover, it was not fine-tuned on the support set similar to the P>M>F method, which could have a huge impact on performance.
 It might also not handle very similar images well.
 
-Compared the the other two methods, CAML performed poorly in almost all experiments.
+Compared to the other two methods, CAML performed poorly in almost all experiments.
 The normal few-shot classification reached only 40% accuracy in @camlperfa at best.
 The only test it did surprisingly well was the detection of the anomaly class for the cable class in @camlperfb were it reached almost 60% accuracy.
 

materialandmethods.typ

@@ -249,7 +249,7 @@ For this bachelor thesis the ResNet-50 architecture was used to predict the corr
 
 === P$>$M$>$F
 // https://arxiv.org/pdf/2204.07305
-P>P>F (Pre-training > Meta-training > Fine-tuning) is a three-stage pipeline designed for few-shot learning.
+P>M>F (Pre-training > Meta-training > Fine-tuning) is a three-stage pipeline designed for few-shot learning.
 It focuses on simplicity but still achieves competitive performance.
 The three stages convert a general feature extractor into a task-specific model through fine-tuned optimization.
 #cite(<pmfpaper>)
@@ -309,7 +309,7 @@ Future research could focus on exploring faster and more efficient methods for f
 
 === CAML <CAML>
 // https://arxiv.org/pdf/2310.10971v2
-CAML (Context aware meta learning) is one of the state-of-the-art methods for few-shot learning.
+CAML (Context-Aware Meta-Learning) is one of the state-of-the-art methods for few-shot learning.
 It consists of three different components: a frozen pre-trained image encoder, a fixed Equal Length and Maximally Equiangular Set (ELMES) class encoder and a non-causal sequence model.
 This is a universal meta-learning approach.
 That means no fine-tuning or meta-training is applied for specific domains.~#cite(<caml_paper>)