GAN with Skip Patch Discriminator for Biological Electron Microscopy
  Image Generation
Nishith Ranjon Roy Nailah Rawnaq Tulin Kaman
Generating realistic electron microscopy (EM) images has been a challenging
problem due to their complex global and local structures. Isola et al. proposed
pix2pix, a conditional Generative Adversarial Network (GAN), for the general
purpose of image-to-image translation; which fails to generate realistic EM
images. We propose a new architecture for the discriminator in the GAN
providing access to multiple patch sizes using skip patches and generating
realistic EM images.