How to Develop an Information Maximizing GAN (InfoGAN) in Keras

Last Updated on September 1, 2020

The Generative Adversarial Network, or GAN, is an architecture for training deep convolutional models for generating synthetic images.

Although remarkably effective, the default GAN provides no control over the types of images that are generated. The Information Maximizing GAN, or InfoGAN for short, is an extension to the GAN architecture that introduces control variables that are automatically learned by the architecture and allow control over the generated image, such as style, thickness, and type in the case of generating images of handwritten digits.

In this tutorial, you will discover how to implement an Information Maximizing Generative Adversarial Network model from scratch.

After completing this tutorial, you will know:

The InfoGAN is motivated by the desire to disentangle and control the properties in generated images.
The InfoGAN involves the addition of control variables to generate an auxiliary model that predicts the control variables, trained via mutual information loss function.
How to develop and train an InfoGAN model from scratch and use the control variables to control which digit is generated by the model.

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Generative Adversarial Networks