{
  "id": "2109.01087",
  "version": "v1",
  "published": "2021-09-02T17:04:18.000Z",
  "updated": "2021-09-02T17:04:18.000Z",
  "title": "On-target Adaptation",
  "authors": [
    "Dequan Wang",
    "Shaoteng Liu",
    "Sayna Ebrahimi",
    "Evan Shelhamer",
    "Trevor Darrell"
  ],
  "categories": [
    "cs.CV",
    "cs.AI",
    "cs.LG"
  ],
  "abstract": "Domain adaptation seeks to mitigate the shift between training on the \\emph{source} domain and testing on the \\emph{target} domain. Most adaptation methods rely on the source data by joint optimization over source data and target data. Source-free methods replace the source data with a source model by fine-tuning it on target. Either way, the majority of the parameter updates for the model representation and the classifier are derived from the source, and not the target. However, target accuracy is the goal, and so we argue for optimizing as much as possible on the target data. We show significant improvement by on-target adaptation, which learns the representation purely from target data while taking only the source predictions for supervision. In the long-tailed classification setting, we show further improvement by on-target class distribution learning, which learns the (im)balance of classes from target data.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-09-02T17:04:18.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "on-target adaptation",
      "target data",
      "source data",
      "source-free methods replace",
      "domain adaptation seeks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}