Generative Adversarial Networks Generative Adversarial Networks
Paper summary 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 high-dimensional 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 **co-dependent** as they are iteratively updated - difficulty: hard to construct likelihood functions for high-dimensional, real-world image data
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Generative Adversarial Networks
Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron Courville and Yoshua Bengio
arXiv e-Print archive - 2014 via Local arXiv
Keywords: stat.ML, cs.LG

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Summary by Tianxiao Zhao 7 months ago
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