How deeply does Grokking Machine Learning cover generative adversarial network?

Defines generator and discriminator roles and their training interaction, but leaves generative learning largely unexplored.

Generative adversarial network

As explained in Grokking Machine Learning

A generative adversarial network is a generative model composed of competing generator and discriminator networks that learn to produce realistic data.

Predictive models answer questions such as whether an image shows a cat or a dog. Generative models instead learn how data looks so they can produce new, similar examples. A generative adversarial network (GAN) uses two competing neural networks: a generator creates realistic-looking images, and a discriminator tries to distinguish generated images from real ones. section 10.5 section 2.3

During training, the two networks interact:

The discriminator receives real images and images made by the generator. section 10.5
The generator aims to make images realistic enough to challenge the discriminator. section 10.5
With a dataset of human faces, this process can produce realistic-looking faces. section 10.5

GANs are one approach to generative machine learning, which is difficult and can require lots of data and computing power. The idea here is intuitive rather than a detailed treatment of how to build or train one. section 2.3 section 10.5

How Grokking Machine Learning covers it

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Related concepts

generatorhas part · A generator is a computational component or construct that produces data or values from inputs, representations, context, or specified constraints.
discriminatorhas part · A discriminator is the model component of a generative adversarial network that distinguishes real data from data generated by its generator.
VQGANincludes · VQGAN is a generative model that combines vector-quantized discrete latent representations with adversarial and perceptual objectives to synthesize images.
generative modelingkind of · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.
DCGAN / deep convolutional ganincludes · A deep convolutional generative adversarial network is a GAN whose generator and discriminator use deep convolutional neural-network architectures.
generationkind of · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.

This explainer was generated by Manning's knowledge graph from the text of Grokking Machine Learning. The section links point to the parts of the book it draws on.