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Generative adversarial network

A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.

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Definition

Mathematical properties

Training and evaluating GAN

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Applications

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Generative adversarial network

Nodes128
Edges127
Triples39
Avg. degree1.98
Density0.015625
Components1

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Generative adversarial network

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related to External links · 31
Generative adversarial network → Aila, Art, Artificial Intelligence, Big Predictions, Computer Vision, Exist, Exist Archived March, Generative Adversarial Networks, Karras, Knight, Laine, LG, MIT Technology Review, NE, Qi, Retrieved January, Samuli, She, Style-Based Generator Architecture, StyleGANThis Cat Does Not

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Important terminology

displaystyle gan mu generator discriminator game ref text omega ln distribution images image sim data training gans probability generate strategy

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
WaveNetinstance ofunlike alternatives such as flow-based generative model.Compared to fully visible belief networks0.80text
PixelRNNinstance ofunlike alternatives such as flow-based generative model.Compared to fully visible belief networks0.80text
autoregressive models in generalinstance ofunlike alternatives such as flow-based generative model.Compared to fully visible belief networks0.80text
GANs can generate one complete sample in one passinstance ofunlike alternatives such as flow-based generative model.Compared to fully visible belief networks0.80text
rather than multiple passes through the network.Compared to Boltzmann machinesinstance ofunlike alternatives such as flow-based generative model.Compared to fully visible belief networks0.80text
linear ICAinstance ofunlike alternatives such as flow-based generative model.Compared to fully visible belief networks0.80text
there is no restriction on the type of function used by the network.Since neural networks are universal approximatorsinstance ofunlike alternatives such as flow-based generative model.Compared to fully visible belief networks0.80text
GANs are asymptotically consistentinstance ofunlike alternatives such as flow-based generative model.Compared to fully visible belief networks0.80text
Generative adversarial networkrelated to External linksArt0.60section
Generative adversarial networkrelated to External linksKnight0.60section
Generative adversarial networkrelated to External linksWill0.60section
Generative adversarial networkrelated to External linksBig Predictions0.60section

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