How to Develop a CycleGAN for Image-to-Image Translation with Keras

Last Updated on September 1, 2020

The Cycle Generative Adversarial Network, or CycleGAN, is an approach to training a deep convolutional neural network for image-to-image translation tasks.

Unlike other GAN models for image translation, the CycleGAN does not require a dataset of paired images. For example, if we are interested in translating photographs of oranges to apples, we do not require a training dataset of oranges that have been manually converted to apples. This allows the development of a translation model on problems where training datasets may not exist, such as translating paintings to photographs.

In this tutorial, you will discover how to develop a CycleGAN model to translate photos of horses to zebras, and back again.

After completing this tutorial, you will know:

• How to load and prepare the horses to zebras image translation dataset for modeling.
• How to train a pair of CycleGAN generator models for translating horses to zebras and zebras to horses.
• How to load saved CycleGAN models and use them to translate photographs.

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.