# 3 infoGAN

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Category: { Machine Learning }
Summary: In GAN, the latent space input is usually random noise, e.g., Gaussian noise. The objective of [[GAN]] GAN The task of GAN is to generate features $X$ from some noise $\xi$ and class labels $Y$, $$\xi, Y \to X.$$ Many different GANs are proposed. Vanilla GAN has a simple structure with a single discriminator and a single generator. It uses the minmax game setup. However, it is not stable to use minmax game to train a GAN model. WassersteinGAN was proposed to solve the stability problem during training1. More advanced GANs like BiGAN and ALI have more complex structures. Vanilla GAN Minmax Game … is a very generic one.
Pages: 3

# 2 f-GAN

Published:
Category: { Machine Learning }
Summary: The essence of [[GAN]] GAN The task of GAN is to generate features $X$ from some noise $\xi$ and class labels $Y$, $$\xi, Y \to X.$$ Many different GANs are proposed. Vanilla GAN has a simple structure with a single discriminator and a single generator. It uses the minmax game setup. However, it is not stable to use minmax game to train a GAN model. WassersteinGAN was proposed to solve the stability problem during training1. More advanced GANs like BiGAN and ALI have more complex structures. Vanilla GAN Minmax Game … is comparing the generated distribution $p_G$ and the data distribution $p_\text{data}$.
Pages: 3

# 1 GAN

Published:
Category: { Machine Learning }
Summary: The task of GAN is to generate features $X$ from some noise $\xi$ and class labels $Y$, $$\xi, Y \to X.$$ Many different GANs are proposed. Vanilla GAN has a simple structure with a single discriminator and a single generator. It uses the minmax game setup. However, it is not stable to use minmax game to train a GAN model. WassersteinGAN was proposed to solve the stability problem during training1. More advanced GANs like BiGAN and ALI have more complex structures. Vanilla GAN Minmax Game Suppose we have two players $G$ and $D$, and a utility $v(D, G)$, a minmax game is maximizing the utility $v(D, G)$ for the worst case of $G=\hat G$ that minimizes $v$ then we have to find $D=\hat D$ that maximizes $v$, i.
Pages: 3