\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}

Active learning can have more influence if the model and the task is more complex and convergence is slower.
The more decision points are required the more active learning can help.

\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.
