{
  "id": "1811.03403",
  "version": "v1",
  "published": "2018-11-08T13:39:49.000Z",
  "updated": "2018-11-08T13:39:49.000Z",
  "title": "ExGate: Externally Controlled Gating for Feature-based Attention in Artificial Neural Networks",
  "authors": [
    "Jarryd Son",
    "Amit Mishra"
  ],
  "categories": [
    "cs.LG",
    "cs.CV",
    "cs.NE",
    "stat.ML"
  ],
  "abstract": "Perceptual capabilities of artificial systems have come a long way since the advent of deep learning. These methods have proven to be effective, however they are not as efficient as their biological counterparts. Visual attention is a set of mechanisms that are employed in biological visual systems to ease computational load by only processing pertinent parts of the stimuli. This paper addresses the implementation of top-down, feature-based attention in an artificial neural network by use of externally controlled neuron gating. Our results showed a 5% increase in classification accuracy on the CIFAR-10 dataset versus a non-gated version, while adding very few parameters. Our gated model also produces more reasonable errors in predictions by drastically reducing prediction of classes that belong to a different category to the true class.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-11-08T13:39:49.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "artificial neural network",
      "feature-based attention",
      "externally controlled gating",
      "ease computational load",
      "classification accuracy"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}