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Field name	Details
Dewey Class	006.31
Title	Generative Adversarial Networks for Image Generation ([EBook] /) / by Xudong Mao, Qing Li.
Author	Mao, Xudong
Added Personal Name	Li, Qing
Other name(s)	SpringerLink (Online service)
Edition statement	1st ed. 2021.
Publication	Singapore : : Springer Singapore : : Imprint: Springer, , 2021.
Physical Details	XII, 77 p. 41 illus., 29 illus. in color. : online resource.
ISBN	9789813360488
Summary Note	Generative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook’s AI research director) as “the most interesting idea in the last 10 years in ML.” GANs’ potential is huge, because they can learn to mimic any distribution of data, which means they can be taught to create worlds similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is remarkable – poignant even. In 2018, Christie’s sold a portrait that had been generated by a GAN for $432,000. Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. However, there are two remaining challenges for GAN image generation: the quality of the generated image and the training stability. This book first provides an overview of GANs, and then discusses the task of image generation and the details of GAN image generation. It also investigates a number of approaches to address the two remaining challenges for GAN image generation. Additionally, it explores three promising applications of GANs, including image-to-image translation, unsupervised domain adaptation and GANs for security. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision. .:
Contents note	Generative Adversarial Networks (GANs) -- GANs for Image Generation -- More Key Applications of GANs -- Conclusions.
Mode of acces to digital resource	Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format.
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site	https://doi.org/10.1007/978-981-33-6048-8
Links to Related Works	Subject References: Application software . Computer and Information Systems Applications . Computer vision . Machine Learning . Authors: author . Li, Qing . Mao, Xudong . Corporate Authors: SpringerLink (Online service) . Classification: 006.31 .

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Generative Adversarial Networks for Image Generation

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