The Discriminator of CycleGan outputs not just a single value to say that the image is real or fake.... But It outputs a grid of numbers (like 8X8 or 7x7), where each number says whether one patch of the input image is fake or real. So, my question is that, why do we do this.. What benefits does it gives us, and what was the problem in the approach where we only outputted a single value?

I'm trying to train a 3D Cycle-GAN on medical image synthesis, more specifically CT to MR. Currently I'm using a 3-Layer Discriminator and a 6 layer UNetGenerator borrowed from the official CycleGAN codes. Same lambda A, B of 10 and .5 of identity. The discriminator loss drops to around 0 in the first few epochs, and the total loss for generator drops to around 1 as well. The generator continues to ouput blurred original input image. During my debugging I …

I am working on a project with facial image translation and GANs and still have some conceptual misunderstandings. In my definition of my model, I extract a deep embedding of my generated image and the input image using a state of the art CNN which I mark as untrainable, calculate the distance between these embeddings and use this distance itself as a loss in my model definition. If the model from which the embeddings come from is untrainable, will the …

I have modified a keras cyclegan keras cyclegan version of horses and zebras to the classical fer2013 face recognition file. I got some results this cyclegan trying to get some additional DISGUST (disgust is the case with fewer samples in the file) faces from NORMALS and I'm trying to modify the discriminator loss with wasserstein as stated here: But after some epochs I see that after every epoch result face is the same no matter the input face is fed. …