write introduction

summary/main.tex

@@ -89,53 +89,36 @@
 \maketitle
 
 \section{Introduction}
-ACM's consolidated article template, introduced in 2017, provides a
-consistent \LaTeX\ style for use across ACM publications, and
-incorporates accessibility and metadata-extraction functionality
-necessary for future Digital Library endeavors. Numerous ACM and
-SIG-specific \LaTeX\ templates have been examined, and their unique
-features incorporated into this single new template.
+For most supervised learning tasks are lots of training samples essential.
+with too less training data the model will gerneralize not well and not fit a real world task.
+Labeling datasets is in commonly seen as an expensive task and wants to be avoided as much as possible.
+Thats why there is a machine-learning field called Semi-Supervised learning.
+The general approach is to train a model that predicts Pseudo-Labels which then can be used to train the main model.
 
-If you are new to publishing with ACM, this document is a valuable
-guide to the process of preparing your work for publication. If you
-have published with ACM before, this document provides insight and
-instruction into more recent changes to the article template.
+\section{Semi-Supervised learning}
+todo write stuff
 
-The ``\verb|acmart|'' document class can be used to prepare articles
-for any ACM publication --- conference or journal, and for any stage
-of publication, from review to final ``camera-ready'' copy, to the
-author's own version, with {\itshape very} few changes to the source.
+\section{FixMatch}\label{sec:fixmatch}
+There exists an already existing approach called FixMatch.
+This was introduced in a Google Research paper from 2020~\cite{fixmatch}.
+
+\section{Cross-Model Pseudo-Labeling}
+todo write stuff \cite{Xu_2022_CVPR}
 
 \section{Math}\label{sec:math}
 \begin{equation}
   \label{eq:equation}
   \mathcal{L}_u = \frac{1}{B_u} \sum_{i=1}^{B_u} \mathbbm{1}(\max(p_i) \geq \tau) \mathcal{H}(\hat{y}_i,F(\mathcal{T}_{\text{strong}}(u_i)))
 \end{equation}
-As noted in the introduction, the ``\verb|acmart|'' document class can
-be used to prepare many different kinds of documentation --- a
-double-blind initial submission of a full-length technical paper, a
-two-page SIGGRAPH Emerging Technologies abstract, a ``camera-ready''
-journal article, a SIGCHI Extended Abstract, and more --- all by
-selecting the appropriate {\itshape template style} and {\itshape
-  template parameters}.
-
-This document will explain the major features of the document
-class. For further information, the {\itshape \LaTeX\ User's Guide} is
-available from
-\url{https://www.acm.org/publications/proceedings-template}.
 
 \section{Figures}
 
-\cite{Xu_2022_CVPR}
-\cite{knuthwebsite}
-
 \begin{figure}[h]
   \centering
   \includegraphics[width=\linewidth]{../presentation/rsc/results}
-  \caption{1907 Franklin Model D roadster. Photograph by Harris \&
-    Ewing, Inc. [Public domain], via Wikimedia
-    Commons. (\url{https://goo.gl/VLCRBB}).}
-  \Description{A woman and a girl in white dresses sit in an open car.}\label{fig:figure}
+  \caption{Performance comparisons between CMPL, FixMatch and supervised learning only}
+  \Description{A woman and a girl in white dresses sit in an open car.}
+  \label{fig:results}
 \end{figure}
 
 %%

summary/sources.bib

@@ -7,7 +7,7 @@
     pages     = {2959-2968}
 }
 
-@online{knuthwebsite,
+@online{fixmatch,
     author = "Kihyuk Sohn, David Berthelot, Chun-Liang Li",
     title = "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence",
     url  = "https://arxiv.org/abs/2001.07685",