{
  "id": "1806.02012",
  "version": "v1",
  "published": "2018-06-06T05:27:38.000Z",
  "updated": "2018-06-06T05:27:38.000Z",
  "title": "A Peek Into the Hidden Layers of a Convolutional Neural Network Through a Factorization Lens",
  "authors": [
    "Uday Singh Saini",
    "Evangelos E. Papalexakis"
  ],
  "categories": [
    "cs.LG",
    "cs.CV",
    "stat.ML"
  ],
  "abstract": "Despite their increasing popularity and success in a variety of supervised learning problems, deep neural networks are extremely hard to interpret and debug: Given and already trained Deep Neural Net, and a set of test inputs, how can we gain insight into how those inputs interact with different layers of the neural network? Furthermore, can we characterize a given deep neural network based on it's observed behavior on different inputs? In this paper we propose a novel factorization based approach on understanding how different deep neural networks operate. In our preliminary results, we identify fascinating patterns that link the factorization rank (typically used as a measure of interestingness in unsupervised data analysis) with how well or poorly the deep network has been trained. Finally, our proposed approach can help provide visual insights on how high-level. interpretable patterns of the network's input behave inside the hidden layers of the deep network.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-06-06T05:27:38.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convolutional neural network",
      "hidden layers",
      "factorization lens",
      "networks input behave inside",
      "deep network"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}