# pix2pix : Image Translation with GAN (2)

Comments# Image-to-Image Translation with Conditional Adversarial Networks (pix2pix)

published to **CVPR2017** by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros

## Learn pair-wise images of $S$ and $T$ like below

- BW & Color image
- Street Scene & Label
- Facade & Label
- Aerial & Map
- Day & Night
- Edges & Photo

source image $x \in S$, target image (label) $y \in T$ is pair-wise.

### thus it is Supervised Learning

## Generator of pix2pix

$G(x,z)$ where $x$: image and $z$: noise

Use U-Net shaped network

- known to be powerful at segmentation task
- use spatial information from features of bottom layer
- use dropout as noise in decoder part

## Discriminator of pix2pix

## Loss function

$x$: source image, $y$: target image, $z$: noise

### Use Adversarial loss and L1 loss

\begin{equation}

\mathcal{L}_{cGAN}(G,D) = \mathbb{E}_{x,y \sim p_{data}(x,y)}[\log D(x,y)] + \mathbb{E}_{x \sim p_{data}(x), z \sim p_z(z)}[\log (1-D(x,G(x,z)))]

\end{equation}

\begin{equation}

\mathcal{L}_{L1}(G) = \mathbb{E}_{x,y \sim p_{data}(x,y),z \sim p_z(z)}[||y-G(x,z)||_1]

\end{equation}

## Result

Do demo!

`https://affinelayer.com/pixsrv/`