finish results section and add most of conclusion and outlook stuff

conclusionandoutlook.typ

@@ -1,5 +1,19 @@
 = Conclusion and Outlook
-
 == 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 or EfficientAD.
+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 scenario 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 the anomaly 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 performed P>M>F performed the best.
+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.
 
 == 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.