https://arxiv.org/pdf/1609.04802v5.pdf
Tensorflow: models
Current models are good at maximising MSE(mean squared error) of the image, however, this metric leads to more smoothing out of the noise and thus leads to loss of detail. A new method of using the 'feature maps' of the VGG(Pretrained loss function CNN) Their GAN architecture works by getting a model to train try and estimate original image and another one trying to descriminate between the the estimate and the original. These two networks train together, the generator being scored on a combination of the VGG mentioned above and the ability of the descriminator to tell the difference.