Description: Generative Adversarial Learning : Architectures and Applications, Hardcover by Razavi-far, Roozbeh (EDT); Ruiz-garcia, Ariel (EDT); Palade, Vasile (EDT); Schmidhuber, Juergen (EDT), ISBN 3030913899, ISBN-13 9783030913892, Brand New, Free shipping in the US 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. Th 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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Book Title: Generative Adversarial Learning : Architectures and Applications
Number of Pages: Xiv, 355 Pages
Language: English
Publication Name: Generative Adversarial Learning: Architectures and Applications
Publisher: Springer International Publishing A&G
Subject: Engineering (General), Intelligence (Ai) & Semantics, Probability & Statistics / General, General
Publication Year: 2022
Type: Textbook
Item Weight: 25.5 Oz
Author: Ariel Ruiz-Garcia
Subject Area: Mathematics, Computers, Technology & Engineering, Science
Item Length: 9.3 in
Item Width: 6.1 in
Series: Intelligent Systems Référence Library
Format: Hardcover
