{
  "id": "1706.02690",
  "version": "v1",
  "published": "2017-06-08T17:43:56.000Z",
  "updated": "2017-06-08T17:43:56.000Z",
  "title": "Principled Detection of Out-of-Distribution Examples in Neural Networks",
  "authors": [
    "Shiyu Liang",
    "Yixuan Li",
    "R. Srikant"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "We consider the problem of detecting out-of-distribution examples in neural networks. We propose ODIN, a simple and effective out-of-distribution detector for neural networks, that does not require any change to a pre-trained model. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions of in- and out-of-distribution samples, allowing for more effective detection. We show in a series of experiments that our approach is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach[1] by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%. We theoretically analyze the method and prove that performance improvement is guaranteed under mild conditions on the image distributions.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-06-08T17:43:56.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural networks",
      "out-of-distribution examples",
      "principled detection",
      "diverse network architectures",
      "softmax score distributions"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}