introduction to generative adversarial networks generative adversarial networks (gans)introduced in by goodfellow et al , are an alternative to vaes for learning latent spaces of images they enable the generation of fairly realistic synthetic images by forcing the generated images to be statistically almost indistinguishable from real ones an intuitive way to understand gans is to imagine forger trying to create fake picasso painting at firstthe forger is pretty bad at the task he mixes some of his fakes with authentic picassos and shows them all to an art dealer the art dealer makes an authenticity assessment for each painting and gives the forger feedback about what makes picasso look like picasso the forger goes back to his studio to prepare some new fakes as times goes onthe forger becomes increasingly competent at imitating the style of picassoand the art dealer becomes increasingly expert at spotting fakes in the endthey have on their hands some excellent fake picassos that' what gan isa forger network and an expert networkeach being trained to best the other as sucha gan is made of two partsgenerator network--takes as input random vector ( random point in the latent space)and decodes it into synthetic image discriminator network (or adversary)--takes as input an image (real or synthetic)and predicts whether the image came from the training set or was created by the generator network the generator network is trained to be able to fool the discriminator networkand thus it evolves toward generating increasingly realistic images as training goes onartificial images that look indistinguishable from real onesto the extent that it' impossible for the discriminator network to tell the two apart (see figure meanwhilethe discriminator is constantly adapting to the gradually improving capabilities of the generatorsetting high bar of realism for the generated images once training is overthe generator is capable of turning any point in its input space into believable image unlike vaesthis latent space has fewer explicit guarantees of meaningful structurein particularit isn' continuous ian goodfellow et al "generative adversarial networks,arxiv ( )licensed to