SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
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Transcript of SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
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SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
Junting Pan Cristian Canton K.McGuinness Noel E. O’Connor Jordi Torres Elisa Sayrol Xavier Giró
ARCHITECTURE OF GENERATOR
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The encoder is initialized with VGG-16, and we do fine tuning of the last two groups of Conv Layers
The decoder is initialized randomly, the last Conv Layer have tanh nonlinearities and the output layer consist in a Conv Layer of kernel size 1x1 with sigmoid activation.
Then according to the post about GAN model we applied the loss function with smaller saliency maps
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SALGAN-GAN: Downsample saliency map
[Inspiration from this blog post]
Compare (BCE)
DownsampledGenerated
Saliency Map
DownsampledGround Truth Saliency Map
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APPLYING GAN - Model Selection
SALICON validation set accuracy metrics for GAN+BCE vs BCE on varying numbers of epochs.
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APPLYING GAN - Model Selection
SALICON validation set Information Gain for different hyper parameter α on varying numbers of epochs