How to Develop an Auxiliary Classifier GAN (AC-GAN) From Scratch with Keras

Last Updated on September 1, 2020

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.

The conditional generative adversarial network, or cGAN for short, is a type of GAN that involves the conditional generation of images by a generator model. Image generation can be conditional on a class label, if available, allowing the targeted generated of images of a given type.

The Auxiliary Classifier GAN, or AC-GAN for short, is an extension of the conditional GAN that changes the discriminator to predict the class label of a given image rather than receive it as input. It has the effect of stabilizing the training process and allowing the generation of large high-quality images whilst learning a representation in the latent space that is independent of the class label.

In this tutorial, you will discover how to develop an auxiliary classifier generative adversarial network for generating photographs of clothing.

After completing this tutorial, you will know:

The auxiliary classifier GAN is a type of conditional GAN that requires that the discriminator predict the class label of a given image.
How to develop generator, discriminator, and composite models for
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Generative Adversarial Networks