  Recent advances in deep learning have pushed the performances of visual
saliency models way further than it has ever been. Numerous models in the
literature present new ways to design neural networks, to arrange gaze pattern
data, or to extract as much high and low-level image features as possible in
order to create the best saliency representation. However, one key part of a
typical deep learning model is often neglected: the choice of the loss
function.
  In this work, we explore some of the most popular loss functions that are
used in deep saliency models. We demonstrate that on a fixed network
architecture, modifying the loss function can significantly improve (or
depreciate) the results, hence emphasizing the importance of the choice of the
loss function when designing a model. We also introduce new loss functions that
have never been used for saliency prediction to our knowledge. And finally, we
show that a linear combination of several well-chosen loss functions leads to
significant improvements in performances on different datasets as well as on a
different network architecture, hence demonstrating the robustness of a
combined metric.
