Generative Adversarial Learning: Architectures and Applications: 217 (Intelligent Systems Reference Library, 217) - Tapa dura
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Sinopsis
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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De la contraportada
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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Generative Adversarial Learning: Architectures and Applications (eng)
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Generative Adversarial Learning Architectures and Applications (eng)
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Generative Adversarial Learning : Architectures and Applications
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Generative Adversarial Learning: Architectures and Applications (Hardcover)
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Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030913899
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Hardcover. Condición: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783030913892
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Generative Adversarial Learning: Architectures and Applications (Intelligent Systems Reference Library, 217)
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Generative Adversarial Learning: Architectures and Applications
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Generative Adversarial Learning : Architectures and Applications
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Generative Adversarial Learning: Architectures and Applications
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Springer International Publishing, 2022
ISBN 10: 3030913899
ISBN 13: 9783030913892
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Condición: 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. Nº de ref. del artículo: 519855799
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Generative Adversarial Learning: Architectures and Applications
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ISBN 10: 3030913899
ISBN 13: 9783030913892
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Buch. Condición: 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. Nº de ref. del artículo: 9783030913892
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