generative deep learning generating images with variational autoencoders sampling from latent space of images to create entirely new images or edit existing ones is currently the most popular and successful application of creative ai in this section and the nextwe'll review some high-level concepts pertaining to image generationalongside implementations details relative to the two main techniques in this domainvariational autoencoders (vaesand generative adversarial networks (gansthe techniques we present here aren' specific to images--you could develop latent spaces of soundmusicor even textusing gans and vaes--but in practicethe most interesting results have been obtained with picturesand that' what we focus on here sampling from latent spaces of images the key idea of image generation is to develop low-dimensional latent space of representations (which naturally is vector spacewhere any point can be mapped to realistic-looking image the module capable of realizing this mappingtaking as input latent point and outputting an image ( grid of pixels)is called generator (in the case of gansor decoder (in the case of vaesonce such latent space has been developedyou can sample points from iteither deliberately or at randomandby mapping them to image spacegenerate images that have never been seen before (see figure training data learning process generator decoder latent space of images ( vector spacefigure artificial image vector from the latent space learning lat ent vector space of imagesand using it sample new images gans and vaes are two different strategies for learning such latent spaces of image representationseach with its own characteristics vaes are great for learning latent spaces that are well structuredwhere specific directions encode meaningful axis of variation in the data gans generate images that can potentially be highly realisticbut the latent space they come from may not have as much structure and continuity licensed to