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\title{Cross-Model Pseudo-Labeling for Semi-Supervised Action recognition}

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\author{Lukas Heiligenbrunner}
\email{k12104785@students.jku.at}
\affiliation{%
  \institution{Johannes Kepler University Linz}
  \city{Linz}
  \state{Upperaustria}
  \country{Austria}
  \postcode{4020}
}

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\renewcommand{\shortauthors}{Trovato and Tobin, et al.}

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\begin{abstract}
  Cross-Model Pseudo-Labeling is a new Framework for generating Pseudo-labels
  for supervised leanring tasks where only a subset of true labels is known.
  It builds upon the existing approach of FixMatch and improves it further by
  using two different sized models complementing each other.
\end{abstract}

%%
%% Keywords. The author(s) should pick words that accurately describe
%% the work being presented. Separate the keywords with commas.
\keywords{neural networks, videos, pseudo-labeling, action recognition}

\received{20 February 2007}
\received[revised]{12 March 2009}
\received[accepted]{5 June 2009}

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\section{Introduction}
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.

\section{Semi-Supervised learning}
todo write stuff

\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}

\section{Figures}

\begin{figure}[h]
  \centering
  \includegraphics[width=\linewidth]{../presentation/rsc/results}
  \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}

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\section{Research Methods}

\subsection{Part One}

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\subsection{Part Two}

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\section{Online Resources}

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congue. Quisque mattis elit a risus ultrices commodo venenatis eget
dui. Etiam sagittis eleifend elementum.

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