Image Synthesis Through Generative Networks

Image Synthesis Through Generative Networks

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1. Introduction to Generative Networks

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.

Course Overview

What Are Generative Networks

Autoencoders Architecture

Variational Autoencoder

Conditional VAE

Diffusion Models

2. VAE implementation

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.

Convolutional Neural Network

Encoder - Decoder Principle

Autoencoder Implementation

VAE implementation

CVAE Implementation

Models Comparison

3. GAN Implementation

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.

Generator - Discriminator Principle

GAN Implementation

Conditional GAN

Most Popular Ready-Made Models

Creating Images with Ready-Made Models

Course Content

Image Synthesis Through Generative Networks

Image Synthesis Through Generative Networks

1. Introduction to Generative Networks

Course Overview What Are Generative Networks Autoencoders Architecture Variational Autoencoder Conditional VAE GANs Diffusion Models

2. VAE implementation

Convolutional Neural Network Encoder - Decoder Principle Autoencoder Implementation VAE implementation CVAE Implementation Models Comparison

3. GAN Implementation

Generator - Discriminator Principle GAN Implementation Conditional GAN GAN vs VAE Most Popular Ready-Made Models Creating Images with Ready-Made Models

Autoencoder Implementation

Finally, we can bring it all together and create an autoencoder that restores the input image with the highest possible quality.

We will use the MNIST dataset because it is relatively simple, and the training time for our network will not be too long.

Note

You can find the source code via the following Link. If you want to run the code or even change some components, you can copy the notebook and work with the copy.

We can see that our model accurately restores handwritten digits.
However, if we attempt to generate new data using samples from a Gaussian distribution, the images appear smoothed and resemble random, unstructured noise.

To address this issue, we need to regularize our latent space by using a Variational Autoencoder (VAE).

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Section 2. Chapter 3

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