{
  "id": "1811.12273",
  "version": "v1",
  "published": "2018-11-29T16:06:57.000Z",
  "updated": "2018-11-29T16:06:57.000Z",
  "title": "On the Transferability of Representations in Neural Networks Between Datasets and Tasks",
  "authors": [
    "Haytham M. Fayek",
    "Lawrence Cavedon",
    "Hong Ren Wu"
  ],
  "comment": "Accepted Paper in the Continual Learning Workshop, NeurIPS 2018",
  "journal": "Continual Learning Workshop, 32nd Neural Information Processing Systems (NeurIPS 2018), Montreal, Canada",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Deep networks, composed of multiple layers of hierarchical distributed representations, tend to learn low-level features in initial layers and transition to high-level features towards final layers. Paradigms such as transfer learning, multi-task learning, and continual learning leverage this notion of generic hierarchical distributed representations to share knowledge across datasets and tasks. Herein, we study the layer-wise transferability of representations in deep networks across a few datasets and tasks and note some interesting empirical observations.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-11-29T16:06:57.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural networks",
      "transferability",
      "deep networks",
      "learn low-level features",
      "share knowledge"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}