Roozbeh Razavi-Far & Ariel Ruiz-Garcia 
Generative Adversarial Learning: Architectures and Applications [PDF ebook] 

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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.

€181.89
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表中的内容

An Introduction to Generative Adversarial Learning: Architectures and Applications.- Generative Adversarial Networks: A Survey on Training, Variants, and Applications.- Fair Data Generation and Machine Learning through Generative Adversarial Networks.

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语言 英语 ● 格式 PDF ● 网页 355 ● ISBN 9783030913908 ● 文件大小 14.2 MB ● 编辑 Roozbeh Razavi-Far & Ariel Ruiz-Garcia ● 出版者 Springer International Publishing ● 市 Cham ● 国家 CH ● 发布时间 2022 ● 下载 24 个月 ● 货币 EUR ● ID 8298967 ● 复制保护 社会DRM

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