# PatchGAN - Type of discriminator - only penalizes structure at the scale of local image patches - tries to classify if each $N \times N$ patch in an image is real or fake - discriminator is run convolutionally across the image, averaging all responses to provide the ultimate output of $D$ - effectively models the image as a Markov random field - assuming independence between pixels separated by more than a patch diameter - type of texture/style loss - rather the regular GAN maps from a 256×256 image to a single scalar output, which signifies “real” or “fake”, whereas the PatchGAN maps from 256×256 to an NxN (here 70×70) array of outputs X, where each $X_{ij}$ signifies whether the patch _ij_ in the image is real or fake. -