bachelor-thesis/conclusionandoutlook.typ

= Conclusion and Outlook <sectionconclusionandoutlook>
== Conclusion
In conclusion one can say that Few-Shot learning is not the best choice for anomaly detection tasks.
It is hugely outperformed by state of the art algorithms like PatchCore@patchcorepaper or EfficientAD@efficientADpaper.
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.
But this should not be the case in most scenarios.
Most of the time plenty of good samples are available and in this case PatchCore or EfficientAD should perform great.

The only case where Few-Shot learning could be used is in a scenarios where one wants to detect the anomaly class itself.
PatchCore and EfficientAD can only detect if an anomaly is present or not but not what type of anomaly it actually is.
So chaining a Few-Shot learner after PatchCore or EfficientAD could be a good idea to use the best of both worlds.

In most of the tests P>M>F@pmfpaper performed the best.
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.

== Outlook
In the future, when new Few-Shot learning methods evolve, it could be interesting to test again how they perform in anomaly detection tasks.
There might be a lack of research in the area where the classes to detect are very similar to each other
and when building a few-shot learning algorithm tailored specifically for very similar classes this could boost the performance by a large margin.

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.
Moreover, TRIDENT (see @TRIDENT) could achieve promising results in an anomaly detection scenario.