keras-io/GauGAN-Image-generation

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

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

[image]

</details>

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