Summary
In this chapter, we applied the concept of generative machine learning to images by generating an image that contains the content of one image and the style of another – a task known as neural style transfer.
In the next chapter, we will expand on this paradigm, where we’ll have a generator that generates fake data and a discriminator that distinguishes fake data from real data. Such models are popularly known as generative adversarial networks (GANs). We will be exploring deep convolutional GANs (DCGANs) in the next chapter.
