{
  "id": "1605.08543",
  "version": "v1",
  "published": "2016-05-27T08:49:21.000Z",
  "updated": "2016-05-27T08:49:21.000Z",
  "title": "Lazy Evaluation of Convolutional Filters",
  "authors": [
    "Sam Leroux",
    "Steven Bohez",
    "Cedric De Boom",
    "Elias De Coninck",
    "Tim Verbelen",
    "Bert Vankeirsbilck",
    "Pieter Simoens",
    "Bart Dhoedt"
  ],
  "categories": [
    "cs.CV",
    "cs.NE"
  ],
  "abstract": "In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network. This allows to trade-off the accuracy of a deep neural network with the computational and memory requirements. This is especially important on a constrained device unable to hold all the weights of the network in memory.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-05-27T08:49:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convolutional filters",
      "lazy evaluation",
      "deep neural network",
      "memory requirements",
      "constrained device"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}