Generative adversarial networks for image generation / Xudong Mao, Qing Li.
Mao, Xudong, author.; Li, Qing, 1962- author.
2021
Q325.5
Available Online

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Title Generative adversarial networks for image generation / Xudong Mao, Qing Li.
Author Mao, Xudong, author.
ISBN 9789813360488 (electronic book)
9813360488 (electronic book)
981336047X
9789813360471
DOI https://doi.org/10.1007/978-981-33-6048-8
Published Singapore : Springer, [2021]
Language English
Description 1 online resource
Item Number 10.1007/978-981-33-6048-8 doi
Call Number Q325.5
Dewey Decimal Classification 006.31
Summary 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 (Facebooks 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, Christies 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
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
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Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 19, 2021).
Added Author Li, Qing, 1962- author.
Available in Other Form Print version: 9789813360471
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Generative Adversarial Networks (GANs)
GANs for Image Generation
More Key Applications of GANs
Conclusions.

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