SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

@article{Pan2017SalGANVS,
  title={SalGAN: Visual Saliency Prediction with Generative Adversarial Networks},
  author={Junting Pan and Cristian Canton-Ferrer and Kevin McGuinness and Noel E. O'Connor and Jordi Torres and Elisa Sayrol and Xavier Gir{\'o}},
  journal={CoRR},
  year={2017},
  volume={abs/1701.01081}
}
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… CONTINUE READING

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SHOWING 1-10 OF 31 REFERENCES

and F

  • Z. Bylinskii, A. Recasens, A. Borji, A. Oliva, A. Torralba
  • Durand. Where should saliency models look next…
  • 2016
Highly Influential
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