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

Creating Images with Ready-Made Models

Let's explore platforms where you can generate images using appropriate models.

OpenArt platform

OpenArt is an AI-driven platform that uses advanced generative models to create diverse and high-quality artworks.
It allows users to input specific style preferences or descriptive prompts to generate unique images. The platform supports a variety of artistic styles, from abstract to photorealistic, making it versatile for different creative needs.

You can find this platform via the Link.

ImagineArt

ImagineArt is another AI-powered platform designed to generate high-quality, artistic images using advanced deep learning techniques. It leverages generative models, particularly Generative Adversarial Networks (GANs), to create artwork in a variety of styles, from abstract and impressionist to photorealistic.

You can find this platform via the Link.
The interface of ImagineArt is simpler than that of OpenArt; you simply write a prompt and click the corresponding button to generate an image.

Canva AI image generator

This is a great option for those already using Canva for design projects. It integrates seamlessly with the platform and allows generation based on their own model called Canva Magic Media.

You can find this platform via the Link.
The interface is quite similar to previous ones: you enter a prompt, select a style, and adjust dimensions such as width and length.

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