{
  "id": "2208.03835",
  "version": "v1",
  "published": "2022-08-07T23:00:40.000Z",
  "updated": "2022-08-07T23:00:40.000Z",
  "title": "How Adversarial Robustness Transfers from Pre-training to Downstream Tasks",
  "authors": [
    "Laura Fee Nern",
    "Yash Sharma"
  ],
  "categories": [
    "cs.LG",
    "cs.AI",
    "stat.ML"
  ],
  "abstract": "Given the rise of large-scale training regimes, adapting pre-trained models to a wide range of downstream tasks has become a standard approach in machine learning. While large benefits in empirical performance have been observed, it is not yet well understood how robustness properties transfer from a pre-trained model to a downstream task. We prove that the robustness of a predictor on downstream tasks can be bound by the robustness of its underlying representation, irrespective of the pre-training protocol. Taken together, our results precisely characterize what is required of the representation function for reliable performance upon deployment.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-08-07T23:00:40.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "downstream task",
      "adversarial robustness transfers",
      "pre-training",
      "robustness properties transfer",
      "pre-trained model"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}