%0 Journal Article %1 pan2017salgan %A Pan, Junting %A Ferrer, Cristian Canton %A McGuinness, Kevin %A O'Connor, Noel E. %A Torres, Jordi %A Sayrol, Elisa %A Giro-i Nieto, Xavier %D 2017 %K deep-learning %T SalGAN: Visual Saliency Prediction with Generative Adversarial Networks %U http://arxiv.org/abs/1701.01081 %X 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.

@article{pan2017salgan, abstract = {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.}, added-at = {2017-06-07T13:58:45.000+0200}, author = {Pan, Junting and Ferrer, Cristian Canton and McGuinness, Kevin and O'Connor, Noel E. and Torres, Jordi and Sayrol, Elisa and Giro-i Nieto, Xavier}, biburl = {https://www.bibsonomy.org/bibtex/27cd93d22a0428b01558425281f6a4e9e/axel.vogler}, description = {SalGAN: Visual Saliency Prediction with Generative Adversarial Networks}, interhash = {fb1c19b51416e121f676656cbb9a95d5}, intrahash = {7cd93d22a0428b01558425281f6a4e9e}, keywords = {deep-learning}, note = {cite arxiv:1701.01081Comment: Our results can be reproduced with the source code and trained models available at https://imatge-upc.github.io/saliency-salgan-2017/}, timestamp = {2017-06-07T13:58:45.000+0200}, title = {SalGAN: Visual Saliency Prediction with Generative Adversarial Networks}, url = {http://arxiv.org/abs/1701.01081}, year = 2017 }