  The prediction of saliency areas in images has been traditionally addressed
with hand crafted features based on neuroscience principles. This paper however
addresses the problem with a completely data-driven approach by training a
convolutional network. The learning process is formulated as a minimization of
a loss function that measures the Euclidean distance of the predicted saliency
map with the provided ground truth. The recent publication of large datasets of
saliency prediction has provided enough data to train a not very deep
architecture which is both fast and accurate. The convolutional network in this
paper, named JuntingNet, won the LSUN 2015 challenge on saliency prediction
with a superior performance in all considered metrics.
