{
  "id": "2006.06113",
  "version": "v1",
  "published": "2020-06-10T23:36:06.000Z",
  "updated": "2020-06-10T23:36:06.000Z",
  "title": "Continual Learning for Affective Computing",
  "authors": [
    "Nikhil Churamani"
  ],
  "comment": "Accepted at the Doctoral Consortium for the IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2020",
  "categories": [
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "Real-world application require affect perception models to be sensitive to individual differences in expression. As each user is different and expresses differently, these models need to personalise towards each individual to adequately capture their expressions and thus model their affective state. Despite high performance on benchmarks, current approaches fall short in such adaptation. In this dissertation, we propose the use of continual learning for affective computing as a paradigm for developing personalised affect perception.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-06-10T23:36:06.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "continual learning",
      "affective computing",
      "current approaches fall short",
      "despite high performance",
      "affect perception models"
    ],
    "tags": [
      "conference paper",
      "dissertation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}