LEADER 03288nam 22005895 450 001 9910483673803321 005 20250315005244.0 010 $a9789813360488 010 $a9813360488 024 7 $a10.1007/978-981-33-6048-8 035 $a(CKB)4100000011773956 035 $a(MiAaPQ)EBC6483659 035 $a(PPN)25385749X 035 $a(DE-He213)978-981-33-6048-8 035 $a(EXLCZ)994100000011773956 100 $a20210217d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGenerative Adversarial Networks for Image Generation /$fby Xudong Mao, Qing Li 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2021. 215 $a1 online resource (86 pages) $cillustrations 311 08$a9789813360471 311 08$a981336047X 320 $aIncludes bibliographical references. 327 $aGenerative Adversarial Networks (GANs) -- GANs for Image Generation -- More Key Applications of GANs -- Conclusions. 330 $aGenerative 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. . 606 $aMachine learning 606 $aComputer vision 606 $aApplication software 606 $aMachine Learning 606 $aComputer Vision 606 $aComputer and Information Systems Applications 615 0$aMachine learning. 615 0$aComputer vision. 615 0$aApplication software. 615 14$aMachine Learning. 615 24$aComputer Vision. 615 24$aComputer and Information Systems Applications. 676 $a006.32 700 $aMao$b Xudong$01221187 702 $aLi$b Qing 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483673803321 996 $aGenerative adversarial networks for image generation$92831551 997 $aUNINA