Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy
  Inference System
  Deep neural networks (DNNs) demonstrate great success in classification
tasks. However, they act as black boxes and we don't know how they make
decisions in a particular classification task. To this end, we propose to
distill the knowledge from a DNN into a fuzzy inference system (FIS), which is
Takagi-Sugeno-Kang (TSK)-type in this paper. The model has the capability to
express the knowledge acquired by a DNN based on fuzzy rules, thus explaining a
particular decision much easier. Knowledge distillation (KD) is applied to
create a TSK-type FIS that generalizes better than one directly from the
training data, which is guaranteed through experiments in this paper. To
further improve the performances, we modify the baseline method of KD and
obtain good results.
