How deeply does Grokking Machine Learning cover discriminator?

Defines the role and training inputs, but does not detail the discriminator’s architecture or objective.

Discriminator

As explained in Grokking Machine Learning

A discriminator is the component of a generative adversarial network that distinguishes real data from synthetic data.

Imagine asking, “Is this a real cat—or a convincing imitation?” In a generative adversarial network (GAN), the discriminator is the component that answers this question. It receives real images and synthetic images produced by the generator, then tries to tell them apart. section 10.5

During GAN training, the two networks work against each other:

The generator creates images intended to look real. section 10.5
The discriminator examines real images alongside generated images. section 10.5
As the competition continues, the generator can produce increasingly realistic images. section 10.5

Human faces

With a dataset of human faces, the discriminator distinguishes genuine faces from generated ones. Try the Which Face Is Real? challenge to test your own judgment. section 10.5

How Grokking Machine Learning covers it

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Also explained in

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Learn Generative AI with PyTorchThe GAN component that learns to distinguish real training images or samples from fake generated ones.
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Inside Deep Learning: Math, Algorithms, ModelsIn a GAN, the neural network that scores whether an example is real or generated.
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Evolutionary Deep Learning: Genetic algorithms and neural networksA GAN discriminator is the neural-network component of a generative adversarial network that distinguishes generated samples from real data.
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Build a Text-to-Image Generator (from Scratch)A discriminator is a neural-network component that distinguishes generated samples from samples drawn from the target data distribution.
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Outlier Detection in PythonA discriminator is the component of a generative adversarial network that distinguishes real observations from generated observations.
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Introduction to Generative AI, Second EditionA discriminator is the component of a generative adversarial network that evaluates whether data is authentic or synthetic.
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Data Without LabelsThe classification component of a generative adversarial network that distinguishes real data from data produced by the generator.

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Related concepts

generative adversarial networkpart of · A generative adversarial network is a generative model with a generator and discriminator trained in competition, enabling the generator to produce samples resembling a target dataset or distribution.
classifierkind of · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
optimizerconfigured by · An optimizer is an algorithm or procedure that adjusts model parameters or other decisions to improve an objective function.
criticalternative to · A critic is the scoring network in a Wasserstein generative adversarial network that assigns realness scores rather than real-or-fake probabilities.
MuseGANhas part · A music-generation GAN variant in this chapter that produces multidimensional representations of musical pieces for later conversion to MIDI.

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.