Let's look at the implementation of the simple GAN based on the MNIST dataset.

>Note
>
>You can find the source code via the following <a 
href="https://colab.research.google.com/drive/1K60cFn9MAQhCUK4j5oE-RIczPDiPUL_I?usp=sharing"
target="_blank"
style="color: #ff8a00; font-weight: 600; text-decoration: none; transition: 0.3s ease-in-out;"
onmouseover="this.style.color='#ff0000'"
onmouseout="this.style.color='#d27204'">Link</a>. If you want to run the code or even change some components, you can copy the notebook and work with the copy.

## Why does training take so long?
We need to train GANs for many more epochs compared to VAEs and their modifications because GAN training involves a **complex adversarial process** between the generator and discriminator, leading to potential instability and slow convergence.     
Additionally, finding the equilibrium where the generator produces realistic data and the discriminator accurately distinguishes between real and fake data requires extensive and **careful tuning over many epochs**.  

However, it's important to note that GANs can generate much more complex images than VAEs, making the lengthy training process worthwhile.

Discover the artistry behind image generation using generative networks in our course. From theory to practice, delve into the world of generative networks, unlocking the secrets to crafting captivating images from data.

In this section, we'll start by understanding different types of generative models. We'll look into autoencoders, which transform data, GANs, which use a competitive approach, and diffusion networks, which spread out data. You'll learn how each model generates data differently, paving the way for further exploration in generative networks.

We will implement a simple Variational Autoencoder (VAE) using the MNIST dataset. We will build and train the VAE model, which includes designing the encoder and decoder networks, sampling from the latent space using the reparameterization trick, and optimizing the combined reconstruction and Kullback-Leibler divergence loss.

Now we will explore the generator-discriminator principle fundamental to GANs, implement a simple GAN model, and examine various GAN variations. Additionally, we will provide an overview of some existing state-of-the-art image generation models.

Image Synthesis Through Generative Networks

GAN Implementation