---
library_name: keras
tags:
- ImageGeneration
- GauGAN
- GAN
- spatially-adaptive normalization
- Encoder
- Segmentation-maps
---

## Model description

In this, GauGAN architecture has been implemented for conditional image generation which was proposed in [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://arxiv.org/abs/1903.07291). 

GauGAN uses a `Generative Adversarial Network (GAN)` to generate realistic images that are conditioned on cue images and segmentation maps.

This repo contains the model for the notebook [**GauGAN for conditional image generation**](https://keras.io/examples/generative/gaugan/)

Full credits go to [Soumik Rakshit](https://github.com/soumik12345) & [Sayak Paul](https://twitter.com/RisingSayak)

## Training and evaluation data

Here, the [Facades dataset](https://cmp.felk.cvut.cz/~tylecr1/facade/) is used for training GauGAN model. Some custom layers that were added into the model are - SPADE (SPatially-Adaptive (DE) normalization), Residual block including SPADE & Gaussian sampler. Also, the GauGAN encoder consists of a few downsampling blocks. It outputs the mean and variance of a distribution as shown in this [image](https://i.imgur.com/JgAv1EW.png).

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

| name | learning_rate | decay | rho | momentum | epsilon | centered | training_precision |
|----|-------------|-----|---|--------|-------|--------|------------------|
|RMSprop|0.0010000000474974513|0.0|0.8999999761581421|0.0|1e-07|False|float32|

 ## Model Plot

<details>
<summary>View Model Plot</summary>

![Model Image](./model.png)

</details>

<center>
Model Reproduced By <u><a href="https://github.com/robotjellyzone"><b>Kavya Bisht</b></a></u>
</center>