"A category of deep
learning neural networks that are composed of two competitive neural networks
together." (Dulani Meedeniya & Iresha Rubasinghe, "A Review of Supportive
Computational Approaches for Neurological Disorder Identification", 2020)
"A powerful machine learning technique made up of two learning systems that compete with each other in a game-like fashion. Features of the winning system are 'genetically' added to the loser along with random mutations. GANs teach themselves through this 'survival of the fittest' evolutionary model. They 'generate' new solutions through many, often millions, of generations." (Scott R Garrigan, "Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools", 2020)
"An artificial intelligence process that includes a 'generator' that produces samples, and a 'discriminator' that differentiates between computer-generated samples and samples derived from 'real-world' sources." (Keram Malicki-Sanchez, "Out of Our Minds: Ontology and Embodied Media in a Post-Human Paradigm", 2020)
"Machine learning framework in which two neural networks compete against each other to win within a gaming environment using a supervised learning pattern." (Jose A R Pinheiro, "Contemporary Imagetics and Post-Images in Digital Media Art: Inspirational Artists and Current Trends (1948-2020)", 2020)
"It refers to a type of neural network that consists of a
generative and a discriminative network that contest with each other especially
in a game scenario. They are used to generate new data that are statistically
similar to the training data." (Vijayaraghavan Varadharajan & J Rian
Leevinson, "Next Generation of Intelligent Cities: Case Studies from Europe", 2021)
"A generative adversarial network, or GAN, is a deep neural
network framework which is able to learn from a set of training data and
generate new data with the same characteristics as the training data." (Thomas Wood)
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