  In the past few years, Generative Adversarial Network (GAN) became a
prevalent research topic. By defining two convolutional neural networks
(G-Network and D-Network) and introducing an adversarial procedure between them
during the training process, GAN has ability to generate good quality images
that look like natural images from a random vector. Besides image generation,
GAN may have potential to deal with wide range of real world problems. In this
paper, we follow the basic idea of GAN and propose a novel model for image
saliency detection, which is called Supervised Adversarial Networks (SAN).
Specifically, SAN also trains two models simultaneously: the G-Network takes
natural images as inputs and generates corresponding saliency maps (synthetic
saliency maps), and the D-Network is trained to determine whether one sample is
a synthetic saliency map or ground-truth saliency map. However, different from
GAN, the proposed method uses fully supervised learning to learn both G-Network
and D-Network by applying class labels of the training set. Moreover, a novel
kind of layer call conv-comparison layer is introduced into the D-Network to
further improve the saliency performance by forcing the high-level feature of
synthetic saliency maps and ground-truthes as similar as possible. Experimental
results on Pascal VOC 2012 database show that the SAN model can generate high
quality saliency maps for many complicate natural images.
