{
  "id": "1704.03817",
  "version": "v1",
  "published": "2017-04-12T16:15:38.000Z",
  "updated": "2017-04-12T16:15:38.000Z",
  "title": "MAGAN: Margin Adaptation for Generative Adversarial Networks",
  "authors": [
    "Ruohan Wang",
    "Antoine Cully",
    "Hyung Jin Chang",
    "Yiannis Demiris"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "We propose a novel training procedure for Generative Adversarial Networks (GANs) to improve stability and performance by using an adaptive hinge loss objective function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive both a principled criterion for updating the margin and an approximate convergence measure. The resulting training procedure is simple yet robust on a diverse set of datasets. We evaluate the proposed training procedure on the task of unsupervised image generation, noting both qualitative and quantitative performance improvements.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-04-12T16:15:38.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "generative adversarial networks",
      "margin adaptation",
      "training procedure",
      "appropriate hinge loss margin",
      "approximate convergence measure"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}