# Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation

arXiv: https://arxiv.org/abs/2012.08459 · HTML: https://mlsanity.com/paper/2012.08459/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [A Voting Approach for Explainable Classification with Rule Learning](https://mlsanity.com/paper/2311.07323/) (2023)
- [Rule Learning by Modularity](https://mlsanity.com/paper/2212.12335/) (2022)
- [Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Case Study on BERT-based Language Models](https://mlsanity.com/paper/2412.10513/) (2024)

## Abstract (arXiv)

Most deep neural networks are considered to be black boxes, meaning their output is hard to interpret. In contrast, logical expressions are considered to be more comprehensible since they use symbols that are semantically close to natural language instead of distributed representations. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and whose complexity depends on the size of the convolutional filter and not on the dimensionality of the input. Our approach is based on rule extraction from binary neural networks with stochastic local search. We show how to extract rules that are not necessarily short, but characteristic of the input, and easy to visualize. Our experiments show that the proposed approach is able to model the functionality of the neural network while at the same time producing interpretable logical rules.