李宏毅GAN学习笔记（02）

GAN Lecture 2

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Conditional Generation by GAN

Algorithm

In each traing iteration:

• Sample m positive examples $\{(c^1,x^1),(c^2,x^2),\dots ,(c^m,x^m)\}$ from database
• Sample m noise samples $\{z^1,z^2,\dots ,z^m\}$ from a distribution
• Obtaining generated data $\{{\tilde{x}}^1,{\tilde{x}}^2,\dots ,{\tilde{x}}^m\}$, ${\tilde{x}}^i=G(c^i,z^i)$
• Sample m objects $\{{\hat{x}}^1,{\hat{x}}^2,\dots ,{\hat{x}}^m\}$ from database
• Update discriminator parameters ${\theta }_d$ to maximize
  • $\tilde{V}=\frac{1}{m}{\sum }_{i=1}^{m}logD(c^i,x^i)+\frac{1}{m}{\sum }_{i=1}^{m}log(1-D(c^i,{\tilde{x}}^i))+{\frac{1}{m}}_{i=1}^{m}log(1-D(c^i,{\hat{x}}^i))$
  • ${\theta }_d\leftarrow {\theta }_d+\eta ▽\tilde{V}({\theta }_d)$

Learning D

• Sample m noise samples $\{z^1,z^2,\dots ,z^m\}$ from a distribution
• Sample m conditions $\{c^1,c^2,\dots ,c^m\}$ from a database
• Update generator parameters ${\theta }_g$ to maximize
  • $\tilde{V}=\frac{1}{m}{\sum }_{i=1}^{m}log(D(G(c^i,z^i)))$, ${\theta }_g\leftarrow \eta ▽\tilde{V}({\theta }_g)$

Learning G

倾向推荐第二种网络架构
参考文献：StackGAN

参考文献：Patch GAN

参考例子：Github