{
  "id": "2306.08733",
  "version": "v1",
  "published": "2023-06-14T20:34:07.000Z",
  "updated": "2023-06-14T20:34:07.000Z",
  "title": "Continuous Learning Based Novelty Aware Emotion Recognition System",
  "authors": [
    "Mijanur Palash",
    "Bharat Bhargava"
  ],
  "comment": "Automatic Emotion Detection, Novelty, Deep Learning",
  "journal": "AAAI Spring Symposium 2022",
  "categories": [
    "cs.CV",
    "cs.LG",
    "cs.MM"
  ],
  "abstract": "Current works in human emotion recognition follow the traditional closed learning approach governed by rigid rules without any consideration of novelty. Classification models are trained on some collected datasets and expected to have the same data distribution in the real-world deployment. Due to the fluid and constantly changing nature of the world we live in, it is possible to have unexpected and novel sample distribution which can lead the model to fail. Hence, in this work, we propose a continuous learning based approach to deal with novelty in the automatic emotion recognition task.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-06-14T20:34:07.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "novelty aware emotion recognition system",
      "continuous learning",
      "closed learning approach",
      "automatic emotion recognition task",
      "novel sample distribution"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}