Knowledge Distillation of Convolutional Neural Networks through Feature
  Map Transformation using Decision Trees
Maddimsetti Srinivas Debdoot Sheet
The interpretation of reasoning by Deep Neural Networks (DNN) is still
challenging due to their perceived black-box nature. Therefore, deploying DNNs
in several real-world tasks is restricted by the lack of transparency of these
models. We propose a distillation approach by extracting features from the
final layer of the convolutional neural network (CNN) to address insights to
its reasoning. The feature maps in the final layer of a CNN are transformed
into a one-dimensional feature vector using a fully connected layer.
Subsequently, the extracted features are used to train a decision tree to
achieve the best accuracy under constraints of depth and nodes. We use the
medical images of dermaMNIST, octMNIST, and pneumoniaMNIST from the medical
MNIST datasets to demonstrate our proposed work. We observed that performance
of the decision tree is as good as a CNN with minimum complexity. The results
encourage interpreting decisions made by the CNNs using decision trees.