A few problems about Feature Patch Discriminator.

[XiaoqiangZhou]

Hi, I'm reading your CSA-inpainting work recently. The most impressive novelty of this work is that CSA layer takes use of information from not only known regions, but also generated contents. A good work~

But I'm a little confused with the feature patch discriminator. Could you please help me?

1. Pretrained? Are shallow layers of the feature patch discriminator pretrained on ImageNet, i.e., you use the shallow layers from pretrained VGG Net?
2. Does the network directly calculate the adversarial loss in 14x14x512 feature map? How do you set the ground truth? a 14x14x512 tensor filled with 0/1? Dose this idea/operation similar with the paper Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting, CVPR2019 . Why you would like to do so, avoid heavy parameters in fully connected layers?

Thx!