fix stuff from prof

materialandmethods.typ

@@ -89,7 +89,7 @@ These models learn a representation of each class in a reduced dimensionality an
   caption: [Prototypical network for 3-ways and 5-shots. #cite(<snell2017prototypicalnetworksfewshotlearning>)],
 ) <prototypefewshot>
 
-The first and easiest method of this bachelor thesis uses a simple ResNet50 to calculate those embeddings and clusters the shots together by calculating the class center.
+The first and easiest method of this bachelor thesis uses a simple ResNet50@resnet to calculate those embeddings and clusters the shots together by calculating the class center.
 This is basically a simple prototypical network.
 See @resnet50impl.~@chowdhury2021fewshotimageclassificationjust
 
@@ -152,12 +152,12 @@ $ <euclideannorm>
 
 === PatchCore
 // https://arxiv.org/pdf/2106.08265
-PatchCore is an advanced method designed for cold-start anomaly detection and localization, primarily focused on industrial image data.
+PatchCore@patchcorepaper is an advanced method designed for cold-start anomaly detection and localization, primarily focused on industrial image data.
 It operates on the principle that an image is anomalous if any of its patches is anomalous.
 The method achieves state-of-the-art performance on benchmarks like MVTec AD with high accuracy, low computational cost, and competitive inference times. #cite(<patchcorepaper>)
 #todo[Absatz umformulieren und vereinfachen]
 
-The PatchCore framework leverages a pretrained convolutional neural network (e.g., WideResNet50) to extract mid-level features from image patches.
+The PatchCore framework leverages a pretrained convolutional neural network (e.g., WideResNet50@resnet) to extract mid-level features from image patches.
 By focusing on intermediate layers, PatchCore balances the retention of localized information with a reduction in bias associated with high-level features pre-trained on ImageNet.
 To enhance robustness to spatial variations, the method aggregates features from local neighborhoods using adaptive pooling, which increases the receptive field without sacrificing spatial resolution. #cite(<patchcorepaper>)
 
@@ -183,7 +183,7 @@ This lowers computational costs while maintaining detection accuracy.~#cite(<pat
 
 === EfficientAD
 // https://arxiv.org/pdf/2303.14535
-EfficientAD is another state of the art method for anomaly detection.
+EfficientAD@efficientADpaper is another state of the art method for anomaly detection.
 It focuses on maintaining performance as well as high computational efficiency.
 At its core, EfficientAD uses a lightweight feature extractor, the Patch Description Network (PDN), which processes images in less than a millisecond on modern hardware.
 In comparison to PatchCore, which relies on a deeper, more computationaly heavy WideResNet-101 network, the PDN uses only four convolutional layers and two pooling layers.
@@ -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>M>F (Pre-training > Meta-training > Fine-tuning) is a three-stage pipeline designed for few-shot learning.
+P>M>F@pmfpaper (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>)
@@ -296,7 +296,7 @@ For a query image the feature extractor extracts its embedding in lower dimensio
 The query image is then assigned to the class with the closest prototype.~#cite(<pmfpaper>)
 
 *Performance:*
-P>M>F performs well across several few-shot learning benchmarks.
+P>M>F@pmfpaper performs well across several few-shot learning benchmarks.
 The combination of pre-training on large dataset and meta-training with episodic tasks helps the model to generalize well.
 The inclusion of fine-tuning enhances adaptability to unseen domains, ensuring robust and efficient learning.~#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)@caml_paper is one of the state-of-the-art methods for few-shot learning.
 It consists of three different components: a frozen pretrained 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>)