add remaining headings and github action workflow

.github/workflows/buildtypst.yml

@@ -0,0 +1,13 @@
+name: Build Typst document
+on: push
+
+jobs:
+  build_typst_documents:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Typst
+        uses: lvignoli/typst-action@main
+        with:
+          source_file: typstalt/main.typ

typstalt/conclusionandoutlook.typ

@@ -0,0 +1,5 @@
+= Conclusion and Outlook
+
+== Conclusion
+
+== Outlook

typstalt/experimentalresults.typ

@@ -0,0 +1,15 @@
+= Experimental Results
+
+== Is Few-Shot learning a suitable fit for anomaly detection?
+
+Should Few-Shot learning be used for anomaly detection tasks?
+How does it compare to well established algorithms such as Patchcore or EfficientAD?
+
+== How does disbalancing the Shot number affect performance?
+Does giving the Few-Shot learner more good than bad samples improve the model performance?
+
+== How does the 3 (ResNet, CAML, \pmf) methods perform in only detecting the anomaly class?
+How much does the performance improve if only detecting an anomaly or not?
+How does it compare to PatchCore and EfficientAD?
+
+== Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?

typstalt/implementation.typ

@@ -0,0 +1,16 @@
+= Implementation
+
+== Experiment Setup
+% todo
+todo setup of experiments, which classes used, nr of samples
+kinds of experiments which lead to graphs
+
+== Jupyter
+
+To get accurate performance measures the active-learning process was implemented in a Jupyter notebook first.
+This helps to choose which of the methods performs the best and which one to use in the final Dagster pipeline.
+A straight forward machine-learning pipeline was implemented with the help of Pytorch and RESNet-18.
+
+Moreover, the Dataset was manually imported with the help of a custom torch dataloader and preprocessed with random augmentations.
+After each loop iteration the Area Under the Curve (AUC) was calculated over the validation set to get a performance measure.
+All those AUC were visualized in a line plot, see section~\ref{sec:experimental-results} for the results.

typstalt/introduction.typ

@@ -25,7 +25,7 @@ How much does the performance improve if only detecting an anomaly or not?
 How does it compare to PatchCore and EfficientAD?
 
 === Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?
-I've tried different distance measures $->$ but results are pretty much the same.
+// I've tried different distance measures $->$ but results are pretty much the same.
 
 == Outline
 todo

typstalt/main.typ

@@ -64,19 +64,15 @@
     v(10mm)
   },
   indent: 2em,
-  depth: 3
+  depth: 2
 )<outline>
 #pagebreak(weak: false)
 
 #include "introduction.typ"
 #include "materialandmethods.typ"
-
+#include "implementation.typ"
-= Section Heading
+#include "experimentalresults.typ"
-#cite(<efficientADpaper>)
+#include "conclusionandoutlook.typ"
-== Subsection Heading
-=== Subsubsection Heading
-==== Paragraph Heading
-===== Subparagraph Heading
 
 #set par(leading: 0.7em, first-line-indent: 0em, justify: true)
 #bibliography("sources.bib", style: "apa")

typstalt/materialandmethods.typ

@@ -7,16 +7,13 @@ MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on
 It contains over 5000 high-resolution images divided into fifteen different object and texture categories.
 Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.
 
-// todo source for https://www.mvtec.com/company/research/datasets/mvtec-ad
+#figure(
-
+  image("rsc/dataset_overview_large.png", width: 80%),
-// todo example image
+  caption: [Architecture convolutional neural network. #cite(<datasetsampleimg>)],
-//\begin{figure}
+) <datasetoverview>
-//    \centering
-//    \includegraphics[width=\linewidth/2]{../rsc/muffin_chiauaua_poster}
-//    \caption{Sample images from dataset. \cite{muffinsvschiuahuakaggle_poster}}
-//    \label{fig:roc-example}
-//\end{figure}
 
+// todo
+Todo: descibe which categories are used in this bac and how many samples there are.
 
 == Methods
 
@@ -37,9 +34,9 @@ The first and easiest method of this bachelor thesis uses a simple ResNet to cal
 See //%todo link to this section
 // todo proper source
 
-=== Generalisation from few samples}
+=== Generalisation from few samples
 
-=== Patchcore}
+=== Patchcore
 
 %todo also show values how they perform on MVTec AD
 

typstalt/sources.bib

@@ -61,6 +61,14 @@
     note = "[Online; accessed 12-April-2024]"
 }
 
+@misc{datasetsampleimg,
+    author = {},
+    title = {{The MVTec anomaly detection dataset (MVTec AD)}},
+    howpublished = "\url{https://www.mvtec.com/company/research/datasets/mvtec-ad}",
+    year = {2024},
+    note = "[Online; accessed 12-April-2024]"
+}
+
 @inproceedings{liang2017soft,
     title={Soft-margin softmax for deep classification},
     author={Liang, Xuezhi and Wang, Xiaobo and Lei, Zhen and Liao, Shengcai and Li, Stan Z},