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fix lots of typos
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2025-02-02 12:59:15 +01:00
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@ -5,16 +5,16 @@
== Is Few-Shot learning a suitable fit for anomaly detection? <expresults2way>
_Should Few-Shot learning be used for anomaly detection tasks?
How does it compare to well established algorithms such as Patchcore or EfficientAD?_
How does it compare to well established algorithms such as PatchCore or EfficientAD?_
@comparison2waybottle shows the performance of the 2-way classification (anomaly or not) on the bottle class and @comparison2waycable the same on the cable class.
The performance values are the same as in @experiments but just merged together into one graph.
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.
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.
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.
#subpar.grid(
figure(image("rsc/comparison-2way-bottle.png"), caption: [