lukas-heiligenbrunner
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43 lines
2.1 KiB
Typst
43 lines
2.1 KiB
Typst
#import "utils.typ": todo
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#import "@preview/subpar:0.1.1"
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= Experimental Results
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== Is Few-Shot learning a suitable fit for anomaly detection?
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_Should Few-Shot learning be used for anomaly detection tasks?
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How does it compare to well established algorithms such as Patchcore or EfficientAD?_
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@comparison2waybottle shows the performance of the 2-way classification (anomaly or not) on the bottle class and @comparison2waycable the same on the cable class.
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The performance values are the same as in @experiments but just merged together into one graph.
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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.
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Both are trained with samples from the 'good' class only.
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So there is a clear performance gap between Few-Shot learning and the state of the art anomaly detection algorithms.
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In the @comparison2way Patchcore and EfficientAD are not included as they aren't directly compareable in the same fashion.
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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.
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#subpar.grid(
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figure(image("rsc/comparison-2way-bottle.png"), caption: [
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Bottle class
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]), <comparison2waybottle>,
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figure(image("rsc/comparison-2way-cable.png"), caption: [
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Cable class
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]), <comparison2waycable>,
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columns: (1fr, 1fr),
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caption: [2-Way classification performance],
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label: <comparison2way>,
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)
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== How does disbalancing the Shot number affect performance?
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_Does giving the Few-Shot learner more good than bad samples improve the model performance?_
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As all three method results in @experiments show, the performance of the Few-Shot learner decreases with an increasing number of good samples.
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Which is an result that is unexpected.
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#todo[Image of disbalanced shots]
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== How does the 3 (ResNet, CAML, pmf) methods perform in only detecting the anomaly class?
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_How much does the performance improve if only detecting an anomaly or not?
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How does it compare to PatchCore and EfficientAD?_
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== Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?
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