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src/materialandmethods.tex

@@ -21,6 +21,23 @@ Each category comprises a set of defect-free training images and a test set of i
 \subsection{Methods}\label{subsec:methods}
 
 \subsubsection{Few-Shot Learning}
+Few-Shot learning is a subfield of machine-learning which aims to train a classification-model with just a few or no samples at all.
+In contrast to traditional supervised learning where a huge amount of labeled data is required is to generalize well to unseen data.
+So the model is prone to overfitting to the few training samples.
+
+Typically a few-shot leaning task consists of a support and query set.
+Where the support-set contains the training data and the query set the evaluation data for real world evaluation.
+A common way to format a few-shot leaning problem is using n-way k-shot notation.
+For Example 3 target classeas and 5 samples per class for training might be a 3-way 5-shot few-shot classification problem.
+
+A classical example of how such a model might work is a prototypical network.
+These models learn a representation of each class and classify new examples based on proximity to these representations in an embedding space.
+
+The first and easiest method of this bachelor thesis uses a simple ResNet to calucalte those embeddings and is basically a simple prototypical netowrk.
+See %todo link to this section
+% todo proper source
+
+\subsubsection{Generalisation from few samples}
 
 \subsubsection{Patchcore}