{
  "id": "1606.01583",
  "version": "v1",
  "published": "2016-06-05T23:42:19.000Z",
  "updated": "2016-06-05T23:42:19.000Z",
  "title": "Semi-Supervised Learning with Generative Adversarial Networks",
  "authors": [
    "Augustus Odena"
  ],
  "comment": "Appearing in the Data Efficient Machine Learning workshop at ICML 2016",
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-06-05T23:42:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "semi-supervised learning",
      "generating higher quality samples",
      "output class labels",
      "extend generative adversarial networks",
      "discriminator network"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}