How to Implement Progressive Growing GAN Models in Keras

The progressive growing generative adversarial network is an approach for training a deep convolutional neural network model for generating synthetic images.

It is an extension of the more traditional GAN architecture that involves incrementally growing the size of the generated image during training, starting with a very small image, such as a 4×4 pixels. This allows the stable training and growth of GAN models capable of generating very large high-quality images, such as images of synthetic celebrity faces with the size of 1024×1024 pixels.

In this tutorial, you will discover how to develop progressive growing generative adversarial network models from scratch with Keras.

After completing this tutorial, you will know:

• How to develop pre-defined discriminator and generator models at each level of output image growth.
• How to define composite models for training the generator models via the discriminator models.
• How to cycle the training of fade-in version and normal versions of models at each level of output image growth.

Kick-start your project with my new book Generative Adversarial Networks with Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

To finish reading, please visit source site