  We introduce SalGAN, a deep convolutional neural network for visual saliency
prediction trained with adversarial examples. The first stage of the network
consists of a generator model whose weights are learned by back-propagation
computed from a binary cross entropy (BCE) loss over downsampled versions of
the saliency maps. The resulting prediction is processed by a discriminator
network trained to solve a binary classification task between the saliency maps
generated by the generative stage and the ground truth ones. Our experiments
show how adversarial training allows reaching state-of-the-art performance
across different metrics when combined with a widely-used loss function like
BCE. Our results can be reproduced with the source code and trained models
available at https://imatge-upc.github.io/saliency-salgan-2017/.
