Generative Adversarial Networks
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
2014

Paper summary
txzhao
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
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

**First published:** 2014/06/10 (5 years ago)

**Abstract:** We propose a new framework for estimating generative models via an
adversarial process, in which we simultaneously train two models: a generative
model G that captures the data distribution, and a discriminative model D that
estimates the probability that a sample came from the training data rather than
G. The training procedure for G is to maximize the probability of D making a
mistake. This framework corresponds to a minimax two-player game. In the space
of arbitrary functions G and D, a unique solution exists, with G recovering the
training data distribution and D equal to 1/2 everywhere. In the case where G
and D are defined by multilayer perceptrons, the entire system can be trained
with backpropagation. There is no need for any Markov chains or unrolled
approximate inference networks during either training or generation of samples.
Experiments demonstrate the potential of the framework through qualitative and
quantitative evaluation of the generated samples.
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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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