We talked about the network dreaming a new scene. Here's another powerful example of the GAN architecture. The Deep Convolution Generative Adversarial Network (DCGAN) architecture allows a neural network to operate in the opposite direction of a typical classifier. An input phrase goes into the network and produces an image output. The network that produces output images is attempting to beat a discriminator based on a classic CNN architecture.

Once the generator gets past a certain point, the discriminator stops training (https://www.slideshare.net/enakai/dcgan-how-does-it-work) and the following image shows how we go from an input to an output image with the DCGAN architecture:

Ultimately, the DCGAN takes in a set of random numbers (or numbers derived from a word, for instance) and produces an image. DCGANs are fun to play with because they learn relationships between an input and their corresponding label. If we attempted to use a word the model has never seen, it'll still produce an output image. I wonder what types of image the model will give us for words it has never seen.