Generative Adversarial Networks For Image Generation

deXudong Mao, Qing Li

|anglais
18 février 2021|
Generative Adversarial Networks For Image Generation de Xudong Mao
Faites vite! Il n’en reste que 3 en ligne!
196,50 $
Couverture rigide
Obtenez 983 points privilègeᴹᴰ
Acheter en ligne
Livraison à une adresse
Expédition gratuite pour les commandes d’au moins 35 $
Cueillette en magasin
Pour savoir si la cueillette en magasin est offerte,
Trouver en magasin
Non vendu en magasin
Les prix et les offres peuvent différer de ceux en magasin

description

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.

Xudong Mao is currently a Postdoctoral Fellow at the Hong Kong Polytechnic University. His research interests are in the areas of computer vision and deep learning, especially generative adversarial networks and unsupervised learning. His research work has been published in top-ranked journals and conferences in the area, such as TPAMI...
Loading
Titre :Generative Adversarial Networks For Image Generation
Format :Couverture rigide
Dimensions de l'article :77 pages, 9.25 X 6.1 X 0 po
Dimensions à l'expédition :77 pages, 9.25 X 6.1 X 0 po
Publié le :18 février 2021
Publié par :Springer Nature
Langue :anglais
Convient aux âges :Tous les âges
ISBN - 13 :9789813360471

Consulté récemment
|