Generative Adversarial Networks.
Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks...
| Publicado en: | Communications of the ACM Vol. 63; no. 11; pp. 139 - 145 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
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Association for Computing Machinery
Nov2020
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=146617580&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 146617580 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Nov2020 vid: 63 iid: 11 pid: 68 pub: Association for Computing Machinery artinfo: ui: 146617580 10.1145/3422622 ppf: 139 ppct: 6 formats: tig: atl: Generative Adversarial Networks. aug: au: Goodfellow, Ian Pouget-Abadie, Jean Mirza, Mehdi Bing Xu Warde-Farley, David Ozair, Sherjil Courville, Aaron Bengio, Yoshua affil: Université de Montréal. su: Generative programming (Computer science) Artificial intelligence Algorithms Game theory sug: subj: Generative programming (Computer science) Artificial intelligence Algorithms Game theory ab: Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic highresolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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