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add info line why pc and eid not in plot
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lukas-heiligenbrunner 2025-01-05 18:02:45 +01:00
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@ -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?_