add patchcore overview

materialandmethods.typ

@@ -98,12 +98,36 @@ This helps the model to learn the underlying patterns and to generalize well to
 In few-shot learning the model has to generalize from just a few samples.
 
 === Patchcore
+PatchCore 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 also show values how they perform on MVTec AD
+// todo vll ersten absatz umofrmulieren und vereinfachen
+The PatchCore framework leverages a pre-trained convolutional neural network (e.g., WideResNet50) 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>)
+
+A crucial component of PatchCore is its memory bank, which stores patch-level features derived from the training dataset.
+This memory bank represents the nominal distribution of features against which test patches are compared.
+To ensure computational efficiency and scalability, PatchCore employs a coreset reduction technique to condense the memory bank by selecting the most representative patch features.
+This optimization reduces both storage requirements and inference times while maintaining the integrity of the feature space. #cite(<patchcorepaper>)
+
+During inference, PatchCore computes anomaly scores by measuring the distance between patch features from test images and their nearest neighbors in the memory bank.
+If any patch exhibits a significant deviation, the corresponding image is flagged as anomalous.
+For localization, the anomaly scores of individual patches are spatially aligned and upsampled to generate segmentation maps, providing pixel-level insights into the anomalous regions. #cite(<patchcorepaper>)
+
+
+Patchcore reaches a 99.6% AUROC on the MVTec AD dataset when detecting anomalies.
+A great advantage of this method is the coreset subsampling reducing the memory bank size significantly.
+This lowers computational costs while maintaining detection accuracy. #cite(<patchcorepaper>)
+
+#figure(
+  image("rsc/patchcore_overview.png", width: 80%),
+  caption: [Architecture of Patchcore. #cite(<patchcorepaper>)],
+) <patchcoreoverview>
 
-=== EfficientAD
-todo stuff #cite(<patchcorepaper>)
 // https://arxiv.org/pdf/2106.08265
+=== EfficientAD
 todo stuff #cite(<efficientADpaper>)
 // https://arxiv.org/pdf/2303.14535