  Although neural networks (especially deep neural networks) have achieved
\textit{better-than-human} performance in many fields, their real-world
deployment is still questionable due to the lack of awareness about the
limitation in their knowledge. To incorporate such awareness in the machine
learning model, prediction with reject option (also known as selective
classification or classification with abstention) has been proposed in
literature. In this paper, we present a systematic review of the prediction
with the reject option in the context of various neural networks. To the best
of our knowledge, this is the first study focusing on this aspect of neural
networks. Moreover, we discuss different novel loss functions related to the
reject option and post-training processing (if any) of network output for
generating suitable measurements for knowledge awareness of the model. Finally,
we address the application of the rejection option in reducing the prediction
time for the real-time problems and present a comprehensive summary of the
techniques related to the reject option in the context of extensive variety of
neural networks. Our code is available on GitHub:
\url{https://github.com/MehediHasanTutul/Reject_option}
