fix stuff from prof
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= Conclusion and Outlook <sectionconclusionandoutlook>
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== Conclusion
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In conclusion one can say that Few-Shot learning is not the best choice for anomaly detection tasks.
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It is hugely outperformed by state of the art algorithms like PatchCore or EfficientAD.
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It is hugely outperformed by state of the art algorithms like PatchCore@patchcorepaper or EfficientAD@efficientADpaper.
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The only benefit of Few-Shot learning is that it can be used in environments where only a limited number of good samples are available.
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But this should not be the case in most scenarios.
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Most of the time plenty of good samples are available and in this case PatchCore or EfficientAD should perform great.
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@ -10,8 +10,8 @@ The only case where Few-Shot learning could be used is in a scenarios where one
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PatchCore and EfficientAD can only detect if an anomaly is present or not but not what type of anomaly it actually is.
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So chaining a Few-Shot learner after PatchCore or EfficientAD could be a good idea to use the best of both worlds.
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In most of the tests P>M>F performed the best.
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But also the simple ResNet50 method performed better than expected in most cases and can be considered if the computational resources are limited and if a simple architecture is enough.
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In most of the tests P>M>F@pmfpaper performed the best.
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But also the simple ResNet50@resnet method performed better than expected in most cases and can be considered if the computational resources are limited and if a simple architecture is enough.
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== Outlook
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In the future, when new Few-Shot learning methods evolve, it could be interesting to test again how they perform in anomaly detection tasks.
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