https://i.imgur.com/lM3EjK9.png
Problem
=============
Label map (semantic segmentation) to realistic image using GANs.

Contributions
=========
1. Coarse-to-fine generator
2. Multi-scale discriminator
3. Robust adversarial learning objective function


Coarse-to-fine Generator
=================
https://i.imgur.com/osEyGOj.png

G1 - Global generator

G2 - Local enhancer

Global Generator:
1. convolutional front-end
2. set of residual blocks
3. transposed convolutional back-end

A semantic label map is passed through the 3 components sequentially 


Local Enhancer:
1. convolutional front-end
2. set of residual blocks
3. transposed convolutional back-end


Training scheme:
1. Train standalone global generator 
2. Freeze global generator weights, train local enhancer
3. Fine-tune all weights together

Multi scale Discriminator
===================
https://i.imgur.com/hNP1cni.png

To allow for global context but work at higher resolution as well, several discriminators are applied at different image scales.


Robust adversarial learning objective function
===============================
https://i.imgur.com/j7CIbV3.png
* Compare original and generated images in feature space at different scales. 
* This is done to ensure more abstract resemblance, not just pixel-space resemblance.
* For feature extraction the discriminator is used.