Course Content
Image Synthesis Through Generative Networks
Image Synthesis Through Generative Networks
1. Introduction to Generative Networks
GAN vs VAE
Let's compare VAE and GAN architectures:
Everything was clear?
Section 3. Chapter 4
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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.
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.
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.
