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GAN  derive backprop signals through a **competitive process** invovling a pair of networks; Aim: provide an overview of GANs for signal processing community, drawing on familiar analogies and concepts; point to remaining challenges in theory and applications. ## Introduction  How to achieve: implicitly modelling highdimensional distributions of data  generator receives **no direct access to real images** but error signal from discriminator  discriminator receives both the synthetic samples and samples drawn from the real images  G: G(z) > R^x, where z \in R^z is a sample from latent space, x \in R^x is an image  D: D(x) > (0, 1). may not be trained in practice until the generator is optimal https://i.imgur.com/wOwSXhy.png ## Preliminaries  objective functions J_G(theta_G;theta_D) and J_D(theta_D;theta_G) are **codependent** as they are iteratively updated  difficulty: hard to construct likelihood functions for highdimensional, realworld image data
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