{
  "id": "2207.07769",
  "version": "v1",
  "published": "2022-07-15T22:04:27.000Z",
  "updated": "2022-07-15T22:04:27.000Z",
  "title": "Anomalous behaviour in loss-gradient based interpretability methods",
  "authors": [
    "Vinod Subramanian",
    "Siddharth Gururani",
    "Emmanouil Benetos",
    "Mark Sandler"
  ],
  "comment": "Accepted at ICLR RobustML workshop 2021",
  "categories": [
    "cs.LG",
    "cs.AI",
    "cs.IR"
  ],
  "abstract": "Loss-gradients are used to interpret the decision making process of deep learning models. In this work, we evaluate loss-gradient based attribution methods by occluding parts of the input and comparing the performance of the occluded input to the original input. We observe that the occluded input has better performance than the original across the test dataset under certain conditions. Similar behaviour is observed in sound and image recognition tasks. We explore different loss-gradient attribution methods, occlusion levels and replacement values to explain the phenomenon of performance improvement under occlusion.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-07-15T22:04:27.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "interpretability methods",
      "anomalous behaviour",
      "loss-gradient attribution methods",
      "image recognition tasks",
      "occluded input"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}