How to Train a Progressive Growing GAN in Keras for Synthesizing Faces
Last Updated on September 1, 2020
Generative adversarial networks, or GANs, are effective at generating high-quality synthetic images.
A limitation of GANs is that the are only capable of generating relatively small images, such as 64×64 pixels.
The Progressive Growing GAN is an extension to the GAN training procedure that involves training a GAN to generate very small images, such as 4×4, and incrementally increasing the size of the generated images to 8×8, 16×16, until the desired output size is met. This has allowed the progressive GAN to generate photorealistic synthetic faces with 1024×1024 pixel resolution.
The key innovation of the progressive growing GAN is the two-phase training procedure that involves the fading-in of new blocks to support higher-resolution images followed by fine-tuning.
In this tutorial, you will discover how to implement and train a progressive growing generative adversarial network for generating celebrity faces.
After completing this tutorial, you will know:
- How to prepare the celebrity faces dataset for training a progressive growing GAN model.
- How to define and train the progressive growing GAN on the celebrity faces dataset.
- How to load saved generator models and use them for generating ad hoc synthetic celebrity faces.
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