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Titlebook: Generative Adversarial Networks for Image Generation; Xudong Mao,Qing Li Book 2021 Springer Nature Singapore Pte Ltd. 2021 Generative Adve

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發(fā)表于 2025-3-21 19:59:19 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Generative Adversarial Networks for Image Generation
編輯Xudong Mao,Qing Li
視頻videohttp://file.papertrans.cn/383/382342/382342.mp4
概述Offers an overview of the theoretical concepts and the current challenges of generative adversarial networks.Proposes advanced GAN image generation approaches with higher image quality and better trai
圖書封面Titlebook: Generative Adversarial Networks for Image Generation;  Xudong Mao,Qing Li Book 2021 Springer Nature Singapore Pte Ltd. 2021 Generative Adve
描述.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 detailsof 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
出版日期Book 2021
關(guān)鍵詞Generative Adversarial Networks; GANs; Adversarial Networks; Image Generation; Generative Models; Image t
版次1
doihttps://doi.org/10.1007/978-981-33-6048-8
isbn_softcover978-981-33-6050-1
isbn_ebook978-981-33-6048-8
copyrightSpringer Nature Singapore Pte Ltd. 2021
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發(fā)表于 2025-3-21 23:08:46 | 只看該作者
Xudong Mao,Qing LiOffers an overview of the theoretical concepts and the current challenges of generative adversarial networks.Proposes advanced GAN image generation approaches with higher image quality and better trai
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Godwell Nhamo,Kaitano Dube,David ChikodziHe et al. 2016), object detection (Ren et al. 2015), and segmentation (Long et al. 2015). Compared with these tasks in supervised learning, however, image generation, which belongs to unsupervised learning, may not achieve the desired performance. The target of image generation is to learn to draw p
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發(fā)表于 2025-3-22 10:56:35 | 只看該作者
https://doi.org/10.1007/978-3-0348-8005-3: image-to-image translation, unsupervised domain adaptation, and GANs for security. One type of GANs application is for tasks that require high-quality images, such as image-to-image translation. To improve the output image quality, a discriminator is introduced to judge whether the output images a
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Generative Adversarial Networks (GANs),ject detection (Ren et al. 2015), and image segmentation (Long et al. 2015). These tasks all fall into the scope of supervised learning, which means that large amounts of labeled data are provided for the learning processes. Compared with supervised learning, however, unsupervised learning shows lit
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