
## 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>
{"library_name": "keras", "tags": ["ImageGeneration", "GauGAN", "GAN", "spatially-adaptive normalization", "Encoder", "Segmentation-maps"]}
keras tensorboard ImageGeneration GauGAN GAN spatially-adaptive normalization Encoder Segmentation-maps arxiv:1903.07291 has_space region:us
TAGS
#keras #tensorboard #ImageGeneration #GauGAN #GAN #spatially-adaptive normalization #Encoder #Segmentation-maps #arxiv-1903.07291 #has_space #region-us 

Model description
-----------------


In this, GauGAN architecture has been implemented for conditional image generation which was proposed in Semantic Image Synthesis with Spatially-Adaptive Normalization.


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


Full credits go to Soumik Rakshit & Sayak Paul


Training and evaluation data
----------------------------


Here, the Facades dataset 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.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model Reproduced By <a href="URL Bisht</b>

### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model Reproduced By <a href="URL Bisht</b>
TAGS
#keras #tensorboard #ImageGeneration #GauGAN #GAN #spatially-adaptive normalization #Encoder #Segmentation-maps #arxiv-1903.07291 #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model Reproduced By <a href="URL Bisht</b>
passage: TAGS
#keras #tensorboard #ImageGeneration #GauGAN #GAN #spatially-adaptive normalization #Encoder #Segmentation-maps #arxiv-1903.07291 #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model Reproduced By <a href="URL Bisht</b>