add generalisation section
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@ -41,6 +41,11 @@ See //%todo link to this section
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=== Generalisation from few samples
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An especially hard task is to generalize from such few samples.
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In typical supervised learning the model sees thousands or millions of samples of the corresponding domain during learning.
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This helps the model to learn the underlying patterns and to generalize well to unseen data.
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In few-shot learning the model has to generalize from just a few samples.
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=== Patchcore
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%todo also show values how they perform on MVTec AD
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@ -88,7 +93,8 @@ This helps to avoid the vanishing gradient problem and helps with the training o
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ResNet has proven to be very successful in many computer vision tasks and is used in this practical work for the classification task.
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There are several different ResNet architectures, the most common are ResNet-18, ResNet-34, ResNet-50, ResNet-101 and ResNet-152. #cite(<resnet>)
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Since the dataset is relatively small and the two class classification task is relatively easy (for such a large model) the ResNet-18 architecture is used in this practical work.
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For this bachelor theis the ResNet-50 architecture was used to predict the corresponding embeddings for the few-shot learning methods.
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=== CAML
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Todo
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