\section{Conclusion and Outlook}\label{sec:conclusion-and-outlook}

\subsection{Conclusion}\label{subsec:conclusion}

Active learning can hugely benefit the learning process when applied correctly.
The lower the batch size $\mathcal{B}$ the more improvement one can expect.
However, the higher the sampling space $\mathcal{S}$ the higher the gains but the more performance is required.

Another possible drawback is that reducing the uncertainty might not always be the best choice.
If a system gets certain about samples that does not always mean this improves the accuracy, since it can simply be certain about the wrong thing. \cite{RubensRecSysHB2010}

\subsection{Outlook}\label{subsec:outlook}

Results might be different with a multiclass classification task and segmentation tasks.
A good point to take over from here is to implement the active learning loop in a real-world scenario where the model is more complex.
Moreover, testing active learning performance within segmentation tasks might be interesting.
