{
  "id": "1703.10355",
  "version": "v1",
  "published": "2017-03-30T08:37:14.000Z",
  "updated": "2017-03-30T08:37:14.000Z",
  "title": "From Deep to Shallow: Transformations of Deep Rectifier Networks",
  "authors": [
    "Senjian An",
    "Farid Boussaid",
    "Mohammed Bennamoun",
    "Jiankun Hu"
  ],
  "comment": "Technical Report",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "In this paper, we introduce transformations of deep rectifier networks, enabling the conversion of deep rectifier networks into shallow rectifier networks. We subsequently prove that any rectifier net of any depth can be represented by a maximum of a number of functions that can be realized by a shallow network with a single hidden layer. The transformations of both deep rectifier nets and deep residual nets are conducted to demonstrate the advantages of the residual nets over the conventional neural nets and the advantages of the deep neural nets over the shallow neural nets. In summary, for two rectifier nets with different depths but with same total number of hidden units, the corresponding single hidden layer representation of the deeper net is much more complex than the corresponding single hidden representation of the shallower net. Similarly, for a residual net and a conventional rectifier net with the same structure except for the skip connections in the residual net, the corresponding single hidden layer representation of the residual net is much more complex than the corresponding single hidden layer representation of the conventional net.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-03-30T08:37:14.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep rectifier networks",
      "corresponding single hidden layer representation",
      "residual net",
      "transformations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}