This chapter illustrates the application of generative adversarial networks (GANs) for generating new images using a practical example. So far in this book, using image data, we have illustrated the use of deep networks for image classification tasks. However, in this chapter, we will explore an interesting and popular approach that helps create new images. Generative adversarial networks have been applied for generating new images, improving image quality, and generating new text and new music. Another interesting application of GANs is in the area of anomaly detection. Here, a GAN is trained to generate data that is considered normal. When this network is used for reconstructing data that is considered not normal or anomalous, the differences in results can help us detect the presence of an anomaly. We will look at an example of generating new images in this chapter.

More specifically, in this chapter, we will cover the following topics:

• Generative adversarial network overview
• Processing MNIST image data 
• Developing the generator network
• Developing the discriminator network
• Training the network
• Reviewing results
• Performance optimization tips and best practices