lukas-heilgenbrunner
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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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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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The only case where Few-Shot learning could be used is in a scenarios where one wants to detect the anomaly class itself.
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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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== 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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There might be a lack of research in the area where the classes to detect are very similar to each other
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and when building a few-shot learning algorithm tailored specifically for very similar classes this could boost the performance by a large margin.
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It might be interesting to test the SOT method (see @SOT) with a ResNet50 feature extractor similar as proposed in this thesis but with SOT for embedding comparison.
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Moreover, TRIDENT (see @TRIDENT) could achive promising results in a anomaly detection scenario.
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