use latexmk

.github/workflows/build.yml

@@ -19,16 +19,13 @@ jobs:
       - name: Install LaTeX
         run: |
           sudo apt-get update
-          sudo apt-get install -y texlive-full biber
+          sudo apt-get install -y texlive-full biber latexmk
 
       # Compile the LaTeX document (first pass)
       - name: Compile LaTeX (first pass)
         run: |
           cd src
-          pdflatex -interaction=nonstopmode -halt-on-error -file-line-error main.tex
-          bibtex sources
-          pdflatex -interaction=nonstopmode -halt-on-error -file-line-error main.tex
-          pdflatex -interaction=nonstopmode -halt-on-error -file-line-error main.tex
+          latexmk -pdf -bibtex -interaction=nonstopmode main.tex
 
       # Upload the compiled PDF as an artifact
       - name: Upload PDF

src/materialandmethods.tex

@@ -20,8 +20,7 @@ Each category comprises a set of defect-free training images and a test set of i
 
 \subsection{Methods}\label{subsec:methods}
 
-\subsubsection{Dagster}
-\subsubsection{Label-Studio}
+\subsubsection{Few-Shot Learning}
 
 \subsubsection{Jupyter Notebook}\label{subsubsec:jupyternb}
 
@@ -64,6 +63,11 @@ There are several different ResNet architectures, the most common are ResNet-18,
 
 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.
 
+\subsubsection{CAML}
+Todo
+\subsubsection{P$>$M$>$F}
+Todo
+
 \subsubsection{Softmax}
 
 The Softmax function~\eqref{eq:softmax}\cite{liang2017soft} converts $n$ numbers of a vector into a probability distribution.