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

Зміст курсу

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

Conditional VAE

A Conditional variational autoencoder (CVAE) is a type of VAE that incorporates a conditional variable into both the encoder and the decoder.
The encoder takes the input data and the conditional variable and produces a latent representation that captures the relevant features of the data given the condition.
The decoder takes the latent representation, the same conditional variable, and generates an output that matches the data and the condition.

What is the Condition?

In a CVAE, the "condition" refers to additional information you provide alongside the regular data. This information helps the model generate data with specific characteristics. Typically, the condition is provided as a label that describes the data.

For example, imagine you're training a CVAE on a dataset of cat and dog images. In this case, the condition would be the image class (cat or dog). You'd feed the image data and a label indicating whether it's a cat or a dog.

Why Do We Need a Condition?

There are two main advantages to incorporating conditions in a CVAE:

Targeted Generation: by providing a specific condition, you can guide the CVAE to generate data that matches that condition. Going back to the cat and dog example, if you provide the "dog" label, the CVAE will focus on generating images that resemble dogs;
Improved Quality and Diversity: conditions are additional constraints for the CVAE's encoder and decoder. This extra information helps the model learn more nuanced data representations, leading to higher quality and diverse outputs. The labels provide a clearer picture of what the model should aim for during reconstruction and generation.

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