{
  "id": "1802.04865",
  "version": "v1",
  "published": "2018-02-13T21:31:36.000Z",
  "updated": "2018-02-13T21:31:36.000Z",
  "title": "Learning Confidence for Out-of-Distribution Detection in Neural Networks",
  "authors": [
    "Terrance DeVries",
    "Graham W. Taylor"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. Closely related to this is the task of out-of-distribution detection, where a network must determine whether or not an input is outside of the set on which it is expected to safely perform. To jointly address these issues, we propose a method of learning confidence estimates for neural networks that is simple to implement and produces intuitively interpretable outputs. We demonstrate that on the task of out-of-distribution detection, our technique surpasses recently proposed techniques which construct confidence based on the network's output distribution, without requiring any additional labels or access to out-of-distribution examples. Additionally, we address the problem of calibrating out-of-distribution detectors, where we demonstrate that misclassified in-distribution examples can be used as a proxy for out-of-distribution examples.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-02-13T21:31:36.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "out-of-distribution detection",
      "learning confidence",
      "out-of-distribution examples",
      "networks output distribution",
      "confidence estimates"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}