# 7 Generative Adversarial Networks

THIS UNIT IS STILL UNDER CONSTRUCTION

• The basic GAN structure and relationship to game-theory. Basic impleminintation. Usage of GANs: Deep fakes, data-augmentation, other uses.

• Generative modelling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.PLAG

• Generative Adversarial Networks (GANs)

• GANs are useful for semi-supervised learning, unsupervised learning, and most notably for generation.

• Latent space

## 7.2 The idea of Generative Adversarial Networks (GAN)

$\min _{G} \max _{D} V(D, G)=\mathbb{E}_{\boldsymbol{x} \sim p_{\text {data }}(\boldsymbol{x})}[\log D(\boldsymbol{x})]+\mathbb{E}_{\boldsymbol{z} \sim p_{\boldsymbol{z}}(\boldsymbol{z})}[\log (1-D(G(\boldsymbol{z})))]$

## 7.4 Quality Measures

• Inception Score
• Fréchet Inception Distance (FID)

## 7.7 Applications

See the GAN ZOO.

they are more resilient to vanishing gradients

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