fix typos

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lukas-heiligenbrunner 2024-05-22 16:46:22 +02:00
parent 8789f83f51
commit 419d06e6b9
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@ -5,7 +5,7 @@ With too less training data the model will not generalize well and not fit a rea
Labeling datasets is commonly seen as an expensive task and wants to be avoided as much as possible.\cite{generalAI} Labeling datasets is commonly seen as an expensive task and wants to be avoided as much as possible.\cite{generalAI}
That's why there is a machine-learning field called active learning. That's why there is a machine-learning field called active learning.
The general approach is to train a model that predicts within every iteration a ranking metric or Pseudo-Labels which then can be used to rank the importance of samples to be labeled by an oracle. The general approach is to train a model that predicts within every iteration a ranking metric or Pseudo-Labels which then can be used to rank the importance of samples to be labeled by an oracle.
These labeled are then used to train the model.\cite{activelearning} These labeled samples are then used to train the model.\cite{activelearning}
The goal of this practical work is to test active learning within a simple classification task and evaluate its performance. The goal of this practical work is to test active learning within a simple classification task and evaluate its performance.
\subsection{Research Questions}\label{subsec:research-questions} \subsection{Research Questions}\label{subsec:research-questions}
@ -25,5 +25,5 @@ In section~\ref{sec:material-and-methods} we talk about general methods and mate
First the problem is modeled mathematically in~\ref{subsubsec:mathematicalmodeling} and then implemented and benchmarked in a Jupyter notebook~\ref{subsubsec:jupyternb}. First the problem is modeled mathematically in~\ref{subsubsec:mathematicalmodeling} and then implemented and benchmarked in a Jupyter notebook~\ref{subsubsec:jupyternb}.
Section~\ref{sec:implementation} gives deeper insights to the implementation for the interested reader with some code snippets. Section~\ref{sec:implementation} gives deeper insights to the implementation for the interested reader with some code snippets.
The experimental results~\ref{sec:experimental-results} are well-presented with clear figures illustrating the performance of active learning across different sample sizes and batch sizes. The experimental results~\ref{sec:experimental-results} are well-presented with clear figures illustrating the performance of active learning across different sample sizes and batch sizes.
The conclusion~\ref{subsec:conclusion} provides a overview of the findings, highlighting the benefits of active learning. The conclusion~\ref{subsec:conclusion} provides an overview of the findings, highlighting the benefits of active learning.
Additionally the outlook section~\ref{subsec:outlook} suggests avenues for future research which are not covered in this work. Additionally the outlook section~\ref{subsec:outlook} suggests avenues for future research which are not covered in this work.

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@ -143,7 +143,7 @@ Finally, after some convolution layers the feature map is flattened and passed t
\begin{figure} \begin{figure}
\centering \centering
\includegraphics[width=\linewidth]{../rsc/cnn_architecture} \includegraphics[width=\linewidth]{../rsc/cnn_architecture}
\caption{Architecture convolutional neural network. Image by \href{https://cointelegraph.com/explained/what-are-convolutional-neural-networks}{SKY ENGINE AI}} \caption{Architecture convolutional neural network. \cite{cnnarchitectureimg}}
\label{fig:cnn-architecture} \label{fig:cnn-architecture}
\end{figure} \end{figure}

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@ -135,3 +135,11 @@ doi = {10.1007/978-0-387-85820-3_23}
archivePrefix={arXiv}, archivePrefix={arXiv},
primaryClass={cs.CV} primaryClass={cs.CV}
} }
@misc{cnnarchitectureimg,
author = {},
title = {{What are convolutional neural networks?}},
howpublished = "\url{https://cointelegraph.com/explained/what-are-convolutional-neural-networks}",
year = {2024},
note = "[Online; accessed 12-April-2024]"
}