generative adversarial network

Also known as GAB, GAN

deep learning method in which two neural networks compete with each other in a game, learning to generate new data with the same statistics as the training set

Research

6,386 papers

Generative adversarial network in medical imaging: A review.Medical image analysis · 2019
Spiking generative adversarial network with attention scoring decoding.Neural networks : the official journal of the International Neural Network Society · 2024
Generative Adversarial Network (GAN) for Simulating Electroencephalography.Brain topography · 2023
A review of generative adversarial network applications in optical coherence tomography image analysis.Journal of optometry · 2022
Generative adversarial network based synthetic data training model for lightweight convolutional neural networks.Multimedia tools and applications · 2023

via PubMed

Wikidata facts

Image: CRAIYON-REALESRGAN-A professional painting of teachers screaming at students about citations in a classroom.jpg

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short name: RGA
Commons category: Generative adversarial networks
Stack Exchange tag: ai.stackexchange.com/tags/generative-adversarial-networks
time of discovery or invention: 2014-06-10

Sources (7)

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Article

An illustration of how a GAN works

A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. 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.