{
  "id": "1711.05914",
  "version": "v1",
  "published": "2017-11-16T04:07:42.000Z",
  "updated": "2017-11-16T04:07:42.000Z",
  "title": "How Generative Adversarial Nets and its variants Work: An Overview of GAN",
  "authors": [
    "Yongjun Hong",
    "Uiwon Hwang",
    "Jaeyoon Yoo",
    "Sungroh Yoon"
  ],
  "categories": [
    "cs.LG"
  ],
  "abstract": "Generative Adversarial Networks gets wide attention in machine learning field because of its massive potential to learn high dimensional, complex real data. Specifically, it does not need to do further distribution assumption and can simply infer real-like samples from latent space. This powerful property leads GAN to be applied various applications such as image synthesis, image attribute editing and semantically decomposing of image. In this review paper, we look into details of GAN that firstly show how it operates and fundamental meaning of objective functions and point to GAN variants applied to vast amount of tasks.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-11-16T04:07:42.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "generative adversarial nets",
      "variants work",
      "learn high dimensional",
      "complex real data",
      "gan variants"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}