  The prediction of salient 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 neural network (convnet). 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 end-to-end architectures that are both fast and accurate. Two
designs are proposed: a shallow convnet trained from scratch, and a another
deeper solution whose first three layers are adapted from another network
trained for classification. To the authors knowledge, these are the first
end-to-end CNNs trained and tested for the purpose of saliency prediction.
