Image Synthesis Through Generative Networks

Kursinhalt

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 GAN

Conditional GANs (cGANs) extend the GAN framework by conditioning both the generator and discriminator on additional information. This auxiliary data can be anything from class labels, images, text, or any other contextual information the model can use to guide the generation process. By doing so, cGANs enable a more controlled and targeted generation of data.

Generator

The generator takes two inputs: a random noise vector and the conditioning information . These inputs are concatenated and fed into the generator network, which outputs the conditionaed generated data.


python

Discriminator

The discriminator also receives two inputs: the data (either real or generated) and the conditioning information. It processes both inputs to determine whether the data is real and if it matches the conditioning information.


python

Data generation

We pass a one-hot-encoded image label as an input of the trained generator network to create new data samples.


python

War alles klar?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 3

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Kursinhalt

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 GAN

Conditional GANs (cGANs) extend the GAN framework by conditioning both the generator and discriminator on additional information. This auxiliary data can be anything from class labels, images, text, or any other contextual information the model can use to guide the generation process. By doing so, cGANs enable a more controlled and targeted generation of data.

Generator

The generator takes two inputs: a random noise vector and the conditioning information . These inputs are concatenated and fed into the generator network, which outputs the conditionaed generated data.


python

Discriminator

The discriminator also receives two inputs: the data (either real or generated) and the conditioning information. It processes both inputs to determine whether the data is real and if it matches the conditioning information.


python

Data generation

We pass a one-hot-encoded image label as an input of the trained generator network to create new data samples.


python

War alles klar?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 3

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