diff --git a/experimentalresults.typ b/experimentalresults.typ
index 9647729..5a9d9c9 100644
--- a/experimentalresults.typ
+++ b/experimentalresults.typ
@@ -12,6 +12,7 @@ The performance values are the same as in @experiments but just merged together
 As a reference Patchcore reaches an AUROC score of 99.6% and EfficientAD reaches 99.8% averaged over all classes provided by the MVTec AD dataset.
 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.
 
@@ -34,7 +35,7 @@ As all three method results in @experiments show, the performance of the Few-Sho
 Which is an result that is unexpected.
 #todo[Image of disbalanced shots]
 
-== How does the 3 (ResNet, CAML, \pmf) methods perform in only detecting the anomaly class?
+== How does the 3 (ResNet, CAML, pmf) methods perform in only detecting the anomaly class?
 _How much does the performance improve if only detecting an anomaly or not?
 How does it compare to PatchCore and EfficientAD?_