{
  "id": "2210.06005",
  "version": "v1",
  "published": "2022-10-12T08:22:12.000Z",
  "updated": "2022-10-12T08:22:12.000Z",
  "title": "Generative Adversarial Nets: Can we generate a new dataset based on only one training set?",
  "authors": [
    "Lan V. Truong"
  ],
  "comment": "Under review for possible publication",
  "categories": [
    "cs.LG",
    "cs.IT",
    "math.IT"
  ],
  "abstract": "A generative adversarial network (GAN) is a class of machine learning frameworks designed by Goodfellow et al. in 2014. In the GAN framework, the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution. GAN generates new samples from the same distribution as the training set. In this work, we aim to generate a new dataset that has a different distribution from the training set. In addition, the Jensen-Shannon divergence between the distributions of the generative and training datasets can be controlled by some target $\\delta \\in [0, 1]$. Our work is motivated by applications in generating new kinds of rice that have similar characteristics as good rice.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-10-12T08:22:12.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "training set",
      "generative adversarial nets",
      "gan framework",
      "similar characteristics",
      "model distribution"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}