# Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy Inference System

arXiv: https://arxiv.org/abs/2010.04974 · HTML: https://mlsanity.com/paper/2010.04974/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions](https://mlsanity.com/paper/2102.04271/) (2021)
- [Scale-Preserving Automatic Concept Extraction (SPACE)](https://mlsanity.com/paper/2308.06022/) (2023)
- [Fuzzy Knowledge Distillation from High-Order TSK to Low-Order TSK](https://mlsanity.com/paper/2302.08038/) (2023)
- [Explainable machine learning workflows for radio astronomical data processing](https://mlsanity.com/paper/2603.16350/) (2026)

## Abstract (arXiv)

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.