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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. 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. 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. 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.
@ -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. Which is an result that is unexpected.
#todo[Image of disbalanced shots] #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 much does the performance improve if only detecting an anomaly or not?
How does it compare to PatchCore and EfficientAD?_ How does it compare to PatchCore and EfficientAD?_