{
  "id": "2007.06712",
  "version": "v1",
  "published": "2020-06-19T15:49:39.000Z",
  "updated": "2020-06-19T15:49:39.000Z",
  "title": "Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI",
  "authors": [
    "Amirhossein Tavanaei"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Understanding intermediate layers of a deep learning model and discovering the driving features of stimuli have attracted much interest, recently. Explainable artificial intelligence (XAI) provides a new way to open an AI black box and makes a transparent and interpretable decision. This paper proposes a new explainable convolutional neural network (XCNN) which represents important and driving visual features of stimuli in an end-to-end model architecture. This network employs encoder-decoder neural networks in a CNN architecture to represent regions of interest in an image based on its category. The proposed model is trained without localization labels and generates a heat-map as part of the network architecture without extra post-processing steps. The experimental results on the CIFAR-10, Tiny ImageNet, and MNIST datasets showed the success of our algorithm (XCNN) to make CNNs explainable. Based on visual assessment, the proposed model outperforms the current algorithms in class-specific feature representation and interpretable heatmap generation while providing a simple and flexible network architecture. The initial success of this approach warrants further study to enhance weakly supervised localization and semantic segmentation in explainable frameworks.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-06-19T15:49:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convolutional networks",
      "embedded encoder-decoder",
      "explainable ai",
      "network employs encoder-decoder neural networks",
      "network architecture"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}