⌕ Поиск по атласу

NIST · ИТ · 07442d41dac5209cb817

07442d41dac5209cb817 · Generative Adversarial Network Performance in Low-Dimensional Settings

Generative Adversarial Network Performance in Low-Dimensional Settings

A generative adversarial network (GAN) is an artificial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in applications

Официальный источник · 2021-04-20

Полные импортированные сведения карточки
{
  "title_en": "Generative Adversarial Network Performance in Low-Dimensional Settings",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2021-04-20",
  "date_original": "April 20, 2021",
  "url": "https://www.nist.gov/publications/generative-adversarial-network-performance-low-dimensional-settings",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Felix M. Jimenez , Amanda Koepke , Mary Gregg , Michael R. Frey",
  "abstract_excerpt_en": "A generative adversarial network (GAN) is an artificial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in applications",
  "id": "07442d41dac5209cb817",
  "sources": [
    "NIST Publications"
  ],
  "source_records": [
    {
      "title_en": "Generative Adversarial Network Performance in Low-Dimensional Settings",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2021-04-20",
      "date_original": "April 20, 2021",
      "url": "https://www.nist.gov/publications/generative-adversarial-network-performance-low-dimensional-settings",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Felix M. Jimenez , Amanda Koepke , Mary Gregg , Michael R. Frey",
      "abstract_excerpt_en": "A generative adversarial network (GAN) is an artificial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in applications"
    }
  ],
  "translation_state": "none"
}