GAN with Keras: Application to Image Deblurring – Sicara Agile Big Data Development

We extract losses at two levels, at the end of the generator and at the end of the full model. The first one is a perceptual loss computed directly on the generator's outputs. This first loss ensures the GAN model is oriented towards a deblurring task. It compares the outputs of the first convolutions of VGG. The second loss is the Wasserstein loss performed on the outputs of the whole model.