{
  "id": "1903.12266",
  "version": "v1",
  "published": "2019-03-16T18:10:35.000Z",
  "updated": "2019-03-16T18:10:35.000Z",
  "title": "Generative Adversarial Networks: recent developments",
  "authors": [
    "Maciej Zamorski",
    "Adrian Zdobylak",
    "Maciej Zięba",
    "Jerzy Świątek"
  ],
  "comment": "10 pages",
  "categories": [
    "cs.LG",
    "cs.CV",
    "stat.ML"
  ],
  "abstract": "In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanatory factors that are hidden in the original data. With the invention of Generative Adversarial Networks (GANs), a subclass of generative models that are able to learn representations in an unsupervised and semi-supervised fashion, we are now able to adversarially learn good mappings from a simple prior distribution to a target data distribution. This paper presents an overview of recent developments in GANs with a focus on learning latent space representations.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-03-16T18:10:35.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "generative adversarial networks",
      "developments",
      "learning latent space representations",
      "target data distribution",
      "simple prior distribution"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 10,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}