  Saliency is the perceptual capacity of our visual system to focus our
attention (i.e. gaze) on relevant objects. Neural networks for saliency
estimation require ground truth saliency maps for training which are usually
achieved via eyetracking experiments. In the current paper, we demonstrate that
saliency maps can be generated as a side-effect of training an object
recognition deep neural network that is endowed with a saliency branch. Such a
network does not require any ground-truth saliency maps for training.Extensive
experiments carried out on both real and synthetic saliency datasets
demonstrate that our approach is able to generate accurate saliency maps,
achieving competitive results on both synthetic and real datasets when compared
to methods that do require ground truth data.
