add image of prototypical network

typstalt/materialandmethods.typ

@@ -30,6 +30,11 @@ For Example 3 target classeas and 5 samples per class for training might be a 3-
 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.
 
+#figure(
+  image("rsc/prototype_fewshot_v3.png", width: 60%),
+  caption: [Prototypical network for few-shots. #cite(<snell2017prototypicalnetworksfewshotlearning>)],
+) <prototypefewshot>
+
 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
@@ -52,7 +57,7 @@ A Jupyter notebook is a shareable document which combines code and its output, t
 The notebook along with the editor provides a environment for fast prototyping and data analysis.
 It is widely used in the data science, mathematics and machine learning community.
 
-In the context of this practical work it can be used to test and evaluate the active learning loop before implementing it in a Dagster pipeline. #cite(<jupyter>)
+In the context of this bachelor thesis it was used to test and evaluate the three few-shot learning methods and to compare them. #cite(<jupyter>)
 
 === CNN
 Convolutional neural networks are especially good model architectures for processing images, speech and audio signals.

typstalt/sources.bib

@@ -98,3 +98,13 @@
     archivePrefix={arXiv},
     primaryClass={cs.CV}
 }
+
+@misc{snell2017prototypicalnetworksfewshotlearning,
+      title={Prototypical Networks for Few-shot Learning},
+      author={Jake Snell and Kevin Swersky and Richard S. Zemel},
+      year={2017},
+      eprint={1703.05175},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1703.05175},
+}