Chapter 8. Generative deep learning


This chapter covers

  • Text generation with LSTM
  • Implementing DeepDream
  • Performing neural style transfer
  • Variational autoencoders
  • Understanding generative adversarial networks

The potential of artificial intelligence to emulate human thought processes goes beyond passive tasks such as object recognition and mostly reactive tasks such as driving a car. It extends well into creative activities. When I first made the claim that in a not-so-distant future, most of the cultural content that we consume will be created with substantial help from AIs, I was met with utter disbelief, even from long-time machine-learning practitioners. That was in 2014. Fast-forward three years, and the disbelief has receded—at an incredible speed. In the summer of 2015, we were entertained by Google’s DeepDream algorithm turning an image into a psychedelic mess of dog eyes and pareidolic artifacts; in 2016, we used the Prisma application to turn photos into paintings of various styles. In the summer of 2016, an experimental short movie, Sunspring, was directed using a script written by a Long Short-Term Memory (LSTM) algorithm—complete with dialogue. Maybe you’ve recently listened to music that was tentatively generated by a neural network.

8.1. Text generation with LSTM

8.2. DeepDream

8.3. Neural style transfer

8.4. Generating images with variational autoencoders

8.5. Introduction to generative adversarial networks