Generative Adversarial Learning: Architectures and Applications (Intelligent Systems Reference Library)

ISBN 10: 3030913929 ISBN 13: 9783030913922

Edit� par Springer, 2023

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This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.

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Titre : Generative Adversarial Learning: ...
�diteur : Springer
Date d'�dition : 2023
Reliure : Couverture souple
Etat : New

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Generative Adversarial Learning: Architectures and Applications

Edit� par Springer International Publishing, 2023

ISBN 10 : 3030913929 ISBN 13 : 9783030913922

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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents high-quality research articles addressing theoretical work for improving the learning process Provides a gentle introduction to GANs and related domainsDescribes most well-known GAN architectures and applications domains. N� de r�f. du vendeur 799921051

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Generative Adversarial Learning: Architectures and Applications

Roozbeh Razavi-Far (u. a.)

Edit� par Springer, 2023

ISBN 10 : 3030913929 ISBN 13 : 9783030913922

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Generative Adversarial Learning: Architectures and Applications

Roozbeh Razavi-Far

Edit� par Springer International Publishing, 2023

ISBN 10 : 3030913929 ISBN 13 : 9783030913922

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs' theoretical developments and their applications. N� de r�f. du vendeur 9783030913922

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Generative Adversarial Learning: Architectures and Applications

Roozbeh Razavi-Far

Edit� par Springer International Publishing, Springer International Publishing Feb 2023, 2023

ISBN 10 : 3030913929 ISBN 13 : 9783030913922

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. Neuware -This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs� theoretical developments and their applications.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 372 pp. Englisch. N� de r�f. du vendeur 9783030913922

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Generative Adversarial Learning: Architectures and Applications

Roozbeh Razavi-Far

Edit� par Springer International Publishing Feb 2023, 2023

ISBN 10 : 3030913929 ISBN 13 : 9783030913922

Neuf Taschenbuch

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs' theoretical developments and their applications. 372 pp. Englisch. N� de r�f. du vendeur 9783030913922

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