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

Ủng hộ

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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Mục lục

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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Ngôn ngữ Anh ● định dạng PDF ● Trang 355 ● ISBN 9783030913908 ● Kích thước tập tin 14.2 MB ● Biên tập viên Roozbeh Razavi-Far & Ariel Ruiz-Garcia ● Nhà xuất bản Springer International Publishing ● Thành phố Cham ● Quốc gia CH ● Được phát hành 2022 ● Có thể tải xuống 24 tháng ● Tiền tệ EUR ● TÔI 8298967 ● Sao chép bảo vệ DRM xã hội

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