  To investigate the use of saliency-map analysis to aid in searches for
transient signals, such as fast radio bursts and individual pulses from radio
pulsars. We aim to demonstrate that saliency maps provide the means to
understand predictions from machine learning algorithms and can be implemented
in piplines used to search for transient events. We have implemented a new deep
learning methodology to predict whether or not any segment of the data contains
a transient event. The algorithm has been trained using real and simulated data
sets. We demonstrate that the algorithm is able to identify such events. The
output results are visually analysed via the use of saliency maps. We find that
saliency maps can produce an enhanced image of any transient feature without
the need for de-dispersion or removal of radio frequency interference. Such
maps can be used to understand which features in the image were used in making
the machine learning decision and to visualise the transient event. Even though
the algorithm reported here was developed to demonstrate saliency-map analysis,
we have detected, in archival data, a single burst event with dispersion
measure of $41$\,cm$^{-3}$pc that is not associated with any currently known
pulsar.
